International Journal of Smart Electrical Engineering
Volume:10 Issue: 2, Spring 2021

In recent years, due to developments in the electricity industry, the use of smart meters has increased worldwide. Smart meters allow the collection of large amounts of microdata on power consumption. The data collected by smart meters can be used in various cases. Since the load duration curve is of great importance in the study of power systems in this paper, the purpose is to obtain the load duration curve of consumer groups using raw smart meter data. The data collected by smart meters reflects the behavior of subscribers, so by categorizing this data, the behavior of subscribers can be categorized, and thus the continuous load curve can be calculated. However, due to challenges such as being raw data collected from smart meters, the presence of anomalous data, and the presence of lost data, the data collected by smart meters need to be pre-processed and corrected. This paper presents an approach for pre-processing and modification of raw data received from smart meters and using them to calculate the load duration curve and other uses. First, the preprocessing and modification of smart meter data on two data sets collected from Alborz Power Distribution Company is done based on the presented method; Then these data are clustered based on the k-means clustering algorithm, and finally, load duration curve is obtained for each cluster.

Over the years, the design and implementation of fault-tolerant circuits have been one of the main concerns of the designers of electronic devices. Quantum Dot Cellular Automata (QCA) is a low-power, compact technology that is prone to various defects due to its small size. We can categorize these defects into three main groups: operational defects, manufacturing defects, and clocking defects. Using redundant cells, fault-tolerant gates, or changing the structure of the gates can improve the overall fault-tolerance of the circuit in some cases. However, increasing the fault-tolerance would lead to an increase in the occupied area and the delay of the gates. Therefore, designing a gate based on intercellular interactions with a minimum number of cells and maximum efficiency, which is also fault-tolerant, is a challenging task. In this paper, we present a new tile-shaped design for XOR and XNOR gates that is robust to the Missing cell, Extra cell, and Rotated cell defects by 25%, 55%, and 25%, respectively. That is why we call these gates TFXOR and TFXNOR, respectively.

Electricity utility have long sought to identify and reduce energy fraud as a significant part of non-technical losses (NTL). Generally, to determine customer’s honesty in consumption on-site inspection is vital. Since, inspecting all customers is expensive, utilities look for new ways to reduce inspection’s range to cases with a higher probability of fraud. One way to reduce the scope of inspection is to use machine learning (ML) algorithms to analysis consumption pattern. But, their performance is not satisfactory due to insufficiency of fraudulent customers. In this paper, a new two-stage ML-based model is presented to detect fraud in distribution network. . In the first stage, an Artificial Neural Network (ANN) is trained to model fraudulent customers, which is used to predict theft scenarios for normal consumers to handle data insufficiency. In the second stage, a Support Vector Machine (SVM) classifier is trained to distinguish normal and suspicious consumers. Assessment and comparison of the proposed algorithm to those of conventional models on a real data set with more than 5000 customers shows its high performance.

In assessing the power system, the most important issue is the system behavior when the system is affected by a disturbance In some plants in case a small event, oscillations are created that are not easily damped and this lack of damping causes the power plant shut down. So, conditions should be set such that the power plant damps the oscillations as soon as possible and returns it to the stable conditions. In this paper, microcontroller-based power system stabilizer (PSS) is designed that increases the single machine dynamic stability of the power system connected to an infinite bus by improving damping in the low frequency oscillation. The system stability is investigated by the eigenvalues and the results of dynamic simulations are provided in the time domain. Programmable interface controllers (PIC) have been used in designing digital PSS; then the continuous time domain PSS is converted to discrete time domain PSS and finally it is implemented on the microcontroller chip.

Recently, wireless sensor networks (WSNs) have been used increasingly in military and civilian fields, especially for target tracking and environmental monitoring purposes. The main obstacle in the way of broader use of WSNs is the limited charge of sensors used in these networks. Research has shown that the power consumption of these sensors can be reduced through the sensible use of routing protocols and multi-hop communications. While cluster-based multi-hop protocols have shown to be very effective in reducing power consumption of sensors, they tend to suffer from power balance and data conflict problems. This paper applied the multi-objective whale optimization algorithm to develop a cluster-based routing protocol for WSNs. For improving the time division multiple access cycle, a bottom-up continuous time slot allocation scheme was used with the purpose of preventing data collision in multi-hop communications, as well as maximizing the sleep period of all idle nodes, including the cluster heads. Simulations performed in MATLAB demonstrated the ability of the developed protocol in prolonging the network lifespan by almost 100% by balancing the power consumption of its sensors.

In this paper, a Fractional Adaptive Fuzzy Controller is designed for controlling blood glucose levels in Type-I diabetic patients. Type 1 diabetes is a chronic disease. In people with type 1 diabetes, the pancreatic cells that make insulin are destroyed, and the body is unable to make insulin. Unlike previous works, the system dynamics is considered as undetermined and in the presence of noise. System dynamics is estimated using the type-2 fuzzy system to eliminate the estimation error through compensation. The adaptive rules for control signal parameters and fuzzy system parameters are determined using Lyapunov stability analysis. A modified Bergman model for a diabetic patient with different conditions is used for evaluating the performance of the proposed controller. Also in addition to the uncertainty of glucose-insulin dynamic, the effect of patient activity and disturbance in terms of lifestyle and type of food consumed is considered, The simulation results show that the proposed controller has very well performance.

A reconfiguration model for the distribution network with the optimization objective of reducing three -phase disequilibrium is suggested in order to cope with an increasingly serious three -phase unbalance in distribution network. Second, the distribution network reconfiguration problem is transformed into a problem of constructing the spanning tree of the graph by evaluating the distribution network in topology, which is solved by the process of breaking -cycle -basis. Then an improved Binary Particle Swarm Optimization (BPSO) algorithm is suggested to solve the reconfiguration problem by randomly choosing the first-branch and canceling the heuristic value of the network, which will extend the search scope and prevent search stagnation. Relevant examples of verification show that, relative to traditional approaches, the proposed algorithm can achieve the best global solution with less computational time and greater probability. The proposed algorithm is tested in 33 bus system and the results show the load balancing in distribution network.

This paper presents an optimization model for the optimal placement of hybrid system of diesel generator, wind turbines and photovoltaic. For this, the proposed objective function has been formulated based on: cost of purchasing equipment, cost of gas split, distance from electrical substation, cost of land acquisition, costs of water split. The calculations are done based on amount of investment for each 3 types of DGs, generating time of each, operation and maintenance costs and bank financial supports (loans). The aim of the optimization process is to design the system with a loss of load probability that is less than 1%. The problem has been optimized by Honey-bee mating optimization (HBMO) algorithm. The honey bee is a social insect that can only survive as a member of a community, or colony. Simulation has been performed on a practical test system in Iran. The results obtained of the proposed algorithm were analyzed and compared with related values of Particle Swam Optimization (PSO) algorithm and related values of genetic algorithm.