مجله کیفیت و بهره وری صنعت برق ایران
سال دهم شماره 1 (پیاپی 22، بهار 1400)

With the increasing reduction of fossil fuels and the increase in pollution caused by fossil fuels, the use of renewable energy has become very important in societies. One of these renewable energies is solar energy. The disadvantage of solar cells is their low power output. To solve this problem, the cells are connected in series and in parallel to increase the level of production power by a solar panel, but despite the maximum power in these mobile production units has been considered by researchers. To solve this problem, and in many articles, maximum tracking methods have been proposed. In this paper, a combined method including fuzzy observation, follow-up, and fuzzy logic has been proposed to increase the production capacity of solar panels. In order to show the performance of the proposed method, the results were compared with the other two methods, which indicated that the performance of the proposed method was satisfactory.

Most Management Scientists believes that “Human Resource” is considered to be an organization’s most important and challenging resource. One of the most critical and complicated duties of the top management is to establish justice in order to retain the required human resources within the organization. Thus the present research was conducted to design and determine a justice-based active human resource persistence model. This Study is an exploratory research using a combination of sequential exploratory (qualitative - quantitative) method. In the qualitative phase Delphi method and in quantitative phase survey-based research method was put into use. In order to collect dada, open questionnaires were utilized in the qualitative phase and closed questionnaires were utilized in quantitative phase respectively. The analysis method was data encoding in qualitative phase and structural equations method in quantitative phase using AMOS software. In the first phase (qualitative phase) the statistical population included 20 experts totally familiar with the research subject and in the second phase (quantitative phase) 377 of Alborz Power distribution company employees. The result indicated that Alborz Power Distribution Company’s justice-based human resources persistence factors are: individual factors, intra-organizational factors, extra-organizational factors, environmental factors, commitment and sense of justice. Moreover, the intra-organizational, extra-organizational, and individual factors influence the sense of justice so that it impacts the organizational commitment. Sense of justice and commitment both affect the persistence of human resources. The environmental factor also plays a moderating role between the human resources persistence and the perceived sense of justice.

Monitoring, control and security of power grids are the responsibility of dispatching centers. Equipment diversity, production constraints, demand, economic efficiency, abundant environmental constraints, and ultimately the human factor are some of the factors that have made these systems complex and dynamic. A review of the literature on the subject shows that researchers have so far focused more on the technical issues and needs of dispatching systems. The structural strategy and operational processes of dispatching systems are less important. The aim of this study is to investigate the management structure of dispatching systems using soft systems (SSM) methodology. This methodology is one of the methods of structuring this type of malfunctioning systems. Using the opinion of experts, this method seeks to create compromise between the components and stakeholders of the system. In this study, the problematic situation was identified using the knowledge of experts and then presented by drawing a clear picture, CATWOE analysis, and root definition of the initial conceptual model, and then the model was compared with the current situation in the real world. And improved identification, and the necessary solutions were proposed to improve the system of dispatching centers.

Nowadays, electricity is one of the most basic needs of human societies, such that almost all industrial operations and a large part of social, economic, and agricultural activities rely on this energy; therefore, the quality and stability of electrical power are important. In this study, we seek to forecast the changes in short-term load consumption concerning the factors affecting electric load, mainly weather changes, as well as the daily and weekly consumption fluctuations that have complex nonlinear relationships. It should be noted that the prediction of short-term changes in load consumption is an essential and critical factor in power distribution systems. The proposed method is a hybrid neural network based on deep learning, which trains with energy consumption data and real weather changes. This neural network is developed by combining CNN and LSTM architectures and optimized for configuration parameters such as the number of network layers and neurons as well as filter size. CNN and LSTM architectures have been utilized to extract existing patterns in data and to predict time series, respectively. The proposed approach predicts the pattern of future consumption by forecasting weather in the next hours and the pattern of electric load consumption in the past hours. Tensorflow framework is employed to implement the proposed approach, and the results are compared with similar state-of-the-art methods. The evaluation results show that the prediction accuracy is improved compared to the best available methods.

Peak-shaving and load leveling have been one of the most important issues of power system.  There are some ways to reduce and smooth out peak loads. With the development of microgrid and smart grid concepts, installation of distributed generation (DG) and renewable energy resources as well as distributed battery storages, which are rapidly increasing on modern distribution grid, have been introduced as one of the solution for this issue. This paper aims to assess the effects of residential battery storage deployment on the peak demand shaving. The problem is formulated as a mixed-integer linear programming (MILP) and implemented in GAMS. The results show that the optimal charge/discharge scheduling of residential battery storages could reduce peak demand and make the load profile smoother.

Desirable lighting is one of the most important factors for optimization of the educational environments. The goal of this paper is to evaluate the lighting of educational environments according to the world standards and make smart lighting for classrooms. For this purpose, classrooms of building A at the Quchan University of Technology is simulated by DIALux software and they examined for glare, energy consumption and uniformity of light on the students workplace. The simulation results and practical measurements show that the glare, uniformity and energy consumption of these classes are not good. To address these problems, an innovative approach is presented for lighting the classrooms by considering the proper lighting on the student’s workplace and whiteboard, glare and lighting uniformity. In addition, an intelligent lighting system is designed for classrooms using motion sensors. This system turn the lamps off and save energy if students are not present in the classroom. Simulation results and practical implementation confirm the effectiveness of the proposed method.

Nowadays, electrical boxes are widely used to hold wires and connections in the electric power industry. A group of these electrical boxes are metal boxes that may be exposed to rusting over time. Therefore, quality control and reliability monitoring in the production of these boxes have become an important challenge that should be fully considered. Many approaches have been proposed by researchers to control the quality characteristics and monitor the reliability of products. In this paper, in order to monitor the rusting time of metal electrical boxes, which are subject to interval censoring, some approaches based on model-based control charts are proposed. Subsequently, the performance of the monitoring schemes are compared with respect to the four indices of average run length, extra quadratic loss, performance comparison, and relative average run length. The results reveal that the proposed cumulative sum control chart performs better than the competing control chart. Finally, in order to confirm the effectiveness of the proposed control chart, a case study in the production of metal electrical boxes is addressed.

Microgrid has a precious role in new power systems. Load balancing, voltage stability and load supporting in peak times are some great issues of microgrid. Besides these advantages, some challenges such as cost of microgrid must be considered. Energy storage system (ESS) and Demand response (DR) program can improve the performance of the microgrid in terms of more voltage stability and cost reduction. In this paper using the renewable distributed generators, ESS and DR program, the cost function is defined where cost minimization of microgrid is performed based on the evolutionary algorithm, bee colony optimization (BCO). It is worth mentioning that battery wear and maintenance cost is also considered in the cost function. Simulation results show that considering the optimized location and capacity of ESS and efficient DR program, the overall cost is noticeably reduced and the microgrid performance is greatly improved.