فصلنامه هوش محاسباتی در مهندسی برق
سال دوم شماره 3 (پاییز 1390)

Nowadays since the speed of reaching to response is getting more important، the random searching algorithms are mostly used instead of the overall search of the problem space. This paper presents the application and the performance comparison of HBMO and PSO optimization techniques for designing an output feedback damping controller for STATCOM in order to enhance the damping of power system. The problem of the damping controller design is considered as an optimization problem with a multi-purpose criterion function and it is solved by HBMO and PSO algorithms. This controller is designed in order to transmit unstable electromechanical modes to the specific area of the complex plane. The proposed controller performance is confirmed by the analysis of the eigenvalue and nonlinear time-domain simulation under various disturbances with both control parameters of STATCOM (capacitor voltage control and terminal voltage control). Simulation results illustrate that the designed controller by HBMO performs better than PSO in finding the solution. Moreover، the design of the controller based on the capacitor voltage control in comparison with terminal voltage control has better low frequency oscillation damping and it increases the dynamical stability of the power system.

In this paper a novel optimization method based on Multi-Objective Gravitational Search Algorithm (MOGSA) is presented for automated design of analog integrated circuits. The recommended method firstly simulates a selected circuit using a simulator and then simulated results are optimized by MOGSA algorithm. Finally this process continues to meet its optimum result. The main programs of the proposed method have been implemented in MATLAB while analog circuits are simulated by HSPICE software. To show the capability of this method، its proficiency will be examined in the optimization of analog integrated circuits design. In this paper، an analog circuit sizing scheme -Optimum Automated Design of a Temperature independent Differential Op-amp using Widlar Current Source- is illustrated as a case study. The computer results obtained from implementing this method indicate that the design specifications are closely met. Moreover، according to various design criteria، this tool by proposing a varied set of answers can give more options to designers to choose a desirable scheme among other suggested results. MOGSA، the proposal algorithm، introduces a novel method in multi objective optimization on the basis of Gravitational Search Algorithm in which the concept of “Pareto-optimality” is used to determine “non-dominated” positions as well as an external repository to keep these positions. To ensure the accuracy of MOGSA performance، this algorithm is validated using several standard test functions from some specialized literatures. Final results indicate that our method is highly competitive with current multi objective optimization algorithms.

This paper presents a New Hybrid Shuffled Frog Leaping (NHSFL) algorithm applied to solve Economic Load Dispatch (ELD) problem. Practical ELD has non-convex cost function and various equality and inequality constraints that convert the ELD problem as a nonlinear، non-convex and non-smooth optimization problem. In this paper، a new frog leaping rule is proposed to improve the local exploration and the performance of the conventional SFL algorithm. Also a genetic mutation operator is used for the creation of new frogs instead of random frog creation that improves the convergence. To show the efficiency of the proposed approach، the non-convex ELD problem is solved using conventional SFL and an improved SFL method proposed by other researchers. Then the results of SFL methods are compared to the results obtained by the proposed NHSFL algorithm. Simulation studies show that the results obtained by NHSFL are more effective and better compared with these algorithms.

Intelligent agents are considered as significant means towards realizing the semantic web vision. On the Semantic Web، integrating ontologies and rules enables software agents to interoperate between them; however، this leads to a problem، that no studies have focused on effective distributed reasoning for integrating ontologies and rules in multiple knowledge-bases. The methods that have been presented for distributed reasoning not only get a lot of times and memory، but also do not lead to a complete and sound reasoning. In this paper، to solve this problem، we present a distributed reasoning system that deals with the representation of the knowledge-base of order sorted logic. This logic is able to describe the hierarchy of predicates and inheritance of expressions that there are in our natural language. To have a distributed reasoning، our proposed method uses the expansion of rigid and valid-non-rigid properties between knowledge-bases. Furthermore، with considering time and the situation of properties for reasoning، the non-rigid properties have not been ignored; in fact، in their valid time and situation، they are used. With this method، we achieve a complete reasoning and، moreover، the extracted knowledge is completely considered in the knowledge-bases and we have a distributed reasoning with high efficiency and sound without missing any information.

In this paper a novel method for detection of power islands based on Discrete Wavelet Transform and Neural Network in a distribution network is investigated. The proposed method has used transient signals generated during an islanding event to detect the formation of the island. The features required for classification were extracted from the transient current and voltage signals through discrete wavelet transform. A neural network classifier is trained to categorize the transient generating events as “islanding” or “non-islanding.”. The proposed technique is tested on CIGRE medium-voltage distribution benchmark system with different distributed generators. The results indicate that this technique can detect islanding events very fast and accurate.

In placing Distributed Generation (DG) sources in distribution networks، a wide range of operational and security issues needs to be considered. Many indices are employed to evaluate the optimum DG placement. Aggregation of indices to obtain a multi-objective index capable of optimizing several weighting factors is a common practice in industry. In this paper، Genetic Algorithm (GA) is successfully used to identify the weighting factors in multi-objective performance index for both IEEE 33-bus and IEEE 69-bus test systems، where DG is extensively located and sized. The multi-objective performance index is computed for each configuration، yielding encouraging simulation results for application of the proposed method in power system optimal placement and sizing of DG.