International Journal of Smart Electrical Engineering
Volume:8 Issue: 2, Spring 2019

The state estimation of a quantized system (Q.S.) is a challenging problem for designing feedback control and model-based fault diagnosis algorithms. The core of a Q.S. is a continuous variable system whose inputs and outputs are represented by their corresponding quantized values. This paper concerns with state estimation of a Q.S. by a qualitative observer. The presented observer in this paper uses a non-deterministic automaton as its qualitative model and estimates quantized values of the system state. Observer inputs are on-line measured input and output signals of Q.S. The previous proposed qualitative observers use dynamics of the continuous variable system of Q.S., whereas, in this paper, the qualitative observer model is built by a quantitative observer. The main theorem of the paper shows that if the parameters of quantitative observer and sampling time are chosen correctly, then qualitative estimation error will be uniformly ultimate bounded, i.e. it will converge to a bounded convex set. In addition, simulation results show that reducing bounds of the convex set results in less additional generated spurious states.

The ability of different Harvey models has been proven for long term forecasting of time series. In this paper a new approach based on modified Harvey model tuned by genetic algorithm is proposed for short term forecasting of electricity price and electricity load. To consider the fluctuate nature of electricity price and electricity consumption, the model consists of some nonlinear terms of forecasts, which the optimal order of the nonlinear terms is determined based on T test and RMSE factor. The optimal order for hourly electricity price and hourly electricity consumption is 3 and 2 nonlinear terms, respectively. The proposed model is applied to the hourly electricity consumption and power market hourly price data for Iran from 22/12/2014-19/02/2015 using statistical analysis software EViews 5. The comparison revealed that the modified Harvey model is a very appropriate candidate for day ahead simultaneous forecasting of hourly electricity price and hourly electricity consumption.

The dynamic stability problem is one of the challenges that is constantly being discussed in power systems. Meanwhile, one of the most important factors which will have a direct impact on its determination is the system state estimation. To monitor the stability of the power system, one of the determinative factors is the accuracy and speed of the state estimation equations’ input data. Therefore, in this paper, the Factorized Load Flow Method was used as a method for estimating input data of the system stability analysis. In this study, factorized load flow method was presented in full details in terms of theoretical relations and simulation results, and in order to prove its performance efficiency a comparison was made between its results with the results of the Newton-Raphson method. The conducted comparisons and investigations showed that the proposed method can determine the needed inputs for state estimation with high speed and precision. The proposed method was simulated using coding environment of MATLAB software and it was shown that this idea enjoyed an appropriate quality for reducing the computational complexity and increasing the accuracy and speed of state estimation.

Recent increment in carbon emission due to the dependency on fossil fuels in power generation sector is a critical issue in the last decade. The motivation to Distributed Generation (DG) in order to catch low carbon networks is rising. This research seeks to experience DG existence in local energy servicing in microgrid structure. Optimal sizing and placement of DG units is followed by this paper for simultaneous power loss reduction and voltage profile improvement. Optimization is solved by applying Limited Constraint Method (LCM) for converting of multi-objective problem to single-objective one. A typical Genetic Algorithm (GA) is presented from the array of artificial intelligence methods for solving the optimization problem. The algorithm is implemented on the IEEE 33 buses standard network. This study is presented in two scenarios, primarily to elaborate the effect of location and determination of DGs has been done to reduce losses and improve the voltage profile. Secondly, the research shows the necessity to load modeling in case of DG presence in networks.

The present study is to investigate and design the logic gates and half adder circuits by using multilayer neural network. The parallel function of the neural networks allows their application in designing high-speed circuits. DSP and FPGA can be used in implementation of these circuits, which reduces the area of the circuit. This study first considers logic gates, and since half adder circuits are the basic systems in computing, a half adder circuit is designed in this study. To design a full adder circuit, two half adders and an OR gate can be used. The results of this study are consistent with the results of gates designed with other technologies such as CMOS and TTL, except that neural networks use less power. The results of the simulations are consistent with the results of logic gates and half adder designed with CMOS and TTL technologies. Matlab 2017 has been used in this paper for simulation.

Ferroresonance is a non-linear resonance which occurs between the capacitors of the cables, lines, and breakers and nonlinear inductance of the reactors and transformers when their core is saturated. Due to ferroresonance, it has been proved that large pulse currents pass through the transformer winding which seems to cause severe electromagnetic forces on the transformer windings. This research work is aimed at characterizing the transformer behavior at ferroresonance mode and calculation of the electromagnetic forces applied on the windings. To this end, a 2D FEM model of a 25KVA distribution transformer is developed. The accuracy of the FEM model is validated by comparing the FEM results and those obtained from the analytical methods. The results of this study are of great importance in the electrical and mechanical design of transformers and their windings.