مجله کیفیت و بهره وری صنعت برق ایران
پیاپی 1 (بهار و تابستان 1391)

In this paper، an improved Artificial Bee Colony (ABC) while called Interactive Artificial Bee Colony (IABC) algorithm is proposed to find the feasible optimal solution of the Economic Load Dispatch (ELD) problem with considering operational constraints of the generators. The aim of ELD problem is to optimum schedule of the online generators generation in the power system for minimizing the total cost of supplying power and satisfying power balance equations subject to operational constraints of the generators such as prohibited operating zones، ramp rate limits، valve-point loading effects، unit generating output limits and multi-fuel options. A generating unit may have prohibited operating zones between the minimum and maximum power outputs due to physical limitations of the power generators. The IABC algorithm describes the food foraging behavior، memorizing and information sharing characteristics of honey bee swarm to find the best possible solution within a reasonable computation time. Effectiveness of the proposed method is demonstrated for three different systems، including 6، 13 and 40 unit generating in comparison with the performance of the other recently optimization algorithms reported in the literature in terms of the solution quality and computation efficiency. The results analysis confirms that the proposed approach has an excellent capability to determine optimal solution of the ELD problems over the other existing methods and enhances efficiently the solutions quality of the power systems.

Recently، Efficiency and reliability improvement of electrical distribution systems have been received a great deal of attentions. Protective and switching devices are one of the most important parameters for improving the system reliability. In most researches carried out about optimization of allocation of protective and switching devices، single objective function has been used. Since by using the single objective function، all indices are not satisfied، in this paper، a novel approach based on multi-objective function has been proposed. The introduced objective function including system average interruption frequency (SAIFI) and total cost. By applying the proposed method، both of mentioned indices are optimally satisfied. The multi-objective optimization has been applied to a typical distribution system. The obtained results in comparison with current condition of system and results of single objective function indicate the effectiveness of the proposed method.

Having electricity with high quality is one of the more important aims in electrical systems. Disturbances in distribution systems can change voltage waveform. There are some methods to prepare high power quality for sensitive loads. In this research، we use “Dynamic Voltage Restorer” to compensate the harmful effects of disturbances on voltage. Since power systems sometimes have complicated dynamic behavior especially during faults; emotional self-tuning controller which is a powerful adaptive controller has been used. In order to improve the performance of this controller from point of view of power quality indices such as flash and sensitive load voltage THD، a new structure is proposed for this controller. Also by this controller، system has better performance during fault conditions. According to simulation results، this controller has better operation than both classical PI controller and emotional controller.

The interminable development of industrialization of nations along with their increasing needs for energy has impelled countries to make some serious policies and enact regulations in order to save energy and raise productivity especially in the electricity industry. To this end، the Ministry of Energy of Iran has provided a number of constructive executive programs and has published them in 2010 in a manuscript named «The First Book». It is crystal clear that these programs have different executive priorities and the degree of their influence on promoting productivity in the electricity industry is not the same. Thus، the purpose of this study is to evaluate and prioritize these executive programs by using the Multi Attribute Utility Theory to help the promotion of productivity in the electricity industry. The data-gathering method of this research is questionnaire in which the opinions of twelve academic and industrial experts in the field of electricity have been used. The prioritization of the programs can assist managers and policy-makers so as to acquire the desirable productivity in the electricity industry by allocating the limited recourses to the programs with high priority.

In this paper، the methods of optimal electrical energy consumption and management are investigated، and their corresponding challenges are evaluated. In general، the optimization methods of electrical energy consumption can be classified into demand side management، the optimization of energy consumption in motors، power quality of electrical loads، and optimization of energy consumption in lightening. Each of these elements and challenges in general، has their own solutions، which are described in this paper. Moreover، in this paper، distributed generation (DG) is introduced as one of the main solution for optimizing electrical energy consumption. Local energy production by renewable-energy-based DGs، non-renewable-energy-based DGs، or hybrid DGS can decrease the transmission line losses and increase the system reliability. Furthermore، the injection of reactive power (correction of power factor) and compensation of system harmonics (like active filters) can be done by DGs. In order to evaluate the solutions which are illustrated in this paper، a grid connected hybrid PV/FC/Battery power system، which is connected to a local load، is simulated in this paper. In this system، not only the load active power is prepared by DG sources، but also the reactive power and system harmonics are compensated. In addition، high performance of power management section، and decreasing the output voltage and current THD can be obtained from simulation results.

Partial discharge is a main source of insulation degradation in power transformers. Therefore accurately locating of partial discharge sources in transformers as the main equipment in power system is needed. This paper proposed two novel methods based on artificial neural networks for partial discharge localization in the power transformers. For this purpose detailed model of transformer and three capacitor model of partial discharge is used. Then impulse test is applied to transformer terminals and current in neutral point is measured for training and test of artificial neural networks. As actual current signals include noise components، the noisy component is added to measured current signals and performance of proposed neural networks for partial discharge localization is shown and results are compared.

Data envelopment analysis (DEA) is a non-parametric approach for measuring the efficiency score of decision making units، which convert a set of inputs to a set of outputs. The conventional DEA models are presented just for discretionary data and data، which are beyond the control of human and for example، are affected by environmental factors، cannot be analyzed in these models. In the recent DEA literature، there are some methods to analyze the efficiency score of production systems with these nondiscretionary data. In this article، we will investigate some of the most influential DEA methods in modeling non-discretionary factors. The presented non-parametric approaches are then applied to the Iranian electricity industry as one of the most effective industries in country development.