High dynamic performance power quality conditioner for AC microgrids

This paper deals with power quality problems encountered in weak AC microgrids and solutions for mitigation. A power electronic converter can be used as an effective power quality conditioner to compensate non-idealities in currents drawn from the grid. A power quality conditioner consisting of three power converters connected to a common DC link is analysed. One of these converters acts as an active power filter for removing unwanted harmonics in grid currents feeding a non-linear load. The other two converters instead remove the harmonics from the voltage at the terminals of a sensitive load. The control of the shunt converter is designed to be fast enough for power quality servicing but also has a fast disturbance rejection capability. Simulation and experimental results validating the concept are provided along with obtained total harmonic distortion improvements

Effect of variable conduction angle on the losses of switched reluctance machine

Switched Reluctance Machine (SRM) is the advanced version of the stepper motor and since last decade, it is of high interest among researchers and industrial applications. Modelling an SRM relies on accurate data and proper selection of parameters. The next step is the performance analysis of the machine for which losses determination is indispensable. Previous studies have shown losses calculation only at a particular instant of switching of the machine (i.e., conduction angle). Therefore, the impact of losses in a motoring region cannot be justified. This paper investigates the impact of varying conduction angle on the performance of machines for a set of switch-on angle. Losses are calculated and predicted through simulating motor operating parameters carried out in MATLAB environment and accuracy of results are compared with experimental results

An Aggregate MapReduce Data Block Placement Strategy for Wireless IoT Edge Nodes in Smart Grid

Big data analytics has simplified processing complexity of large dataset in a distributed environment. Many state-of-the-art platforms i.e. smart grid has adopted the processing structure of big data and manages a large volume of data through MapReduce paradigm at distribution ends. Thus, whenever a wireless IoT edge node bundles a sensor dataset into storage media, MapReduce agent performs analytics and generates output into the grid repository. This practice has efficiently reduced the consumption of resources in such a giant network and strengthens other components of the smart grid to perform data analytics through aggregate programming. However, it consumes an operational latency of accessing large dataset from a central repository. As we know that, smart grid processes I/O operations of multi-homing networks, therefore, it accesses large datasets for processing MapReduce jobs at wireless IoT edge nodes. As a result, aggregate MapReduce at wireless IoT edge node produces a network congestion and operational latency problem. To overcome this issue, we propose Wireless IoT Edge-enabled Block Replica Strategy (WIEBRS), that stores in-place, partition-based and multi-homing block replica to respective edge nodes. This reduces the delay latency of accessing datasets for aggregate MapReduce and increases the performance of the job in the smart grid. The simulation results show that WIEBRS effective decreases operational latency with an increment of aggregate MapReduce job performance in the smart grid

Steady State Dynamic Operating Behavior of Universal Motor

A detailed investigation of the universal motor is developed and used for various dynamic steady state and transient operating conditions of loads. In the investigation, output torque, motor speed, input current, input/output power and efficiency are computed, compared and analyzed for different loads. While this paper discusses the steady-state behavior of the universal motor, another companion paper, ?Transient dynamic behavior of universal motor?, will discuss its transient behavior in detail. A non-linear generalized electric machine model of the motor is considered for the analysis. This study was essential to investigate effect of output load on input current, power, speed and efficiency of the motor during operations. Previously such investigation is not know

Application of Generalized Machine Theory in Analysis of Behavior of Universal Motor

This paper teaches GME (Generalized Machine Theory) while investigating into Universal Motor?s Dynamic Behavior for both transient and steady-state on various load applications. Formation of GME Equations of the Universal Motor is explained by formation of the transient equations of equivalent circuit of the universal motor, by applying Kirchhoff?s Loop Law. They are numerically solved for transient behavior. After that the transient equations of the motor are converted into steady-state equivalent equations by replacing instantaneous currents and voltages into their rms values and substituting and then etc. and solving for steady-state solution. In each dynamic switching, transient and steady-state analysis, input power, current, power factor and speed of the motor are determined and plotted against common time and then analyzed. This research was essential to investigate the effect of applied load on input and output behavior of Universal Motor. Up to now, nobody has attempted to find effect of applied loads on performance of the Universal Motor. The results obtained for the dynamic steady state and transient characteristics are presented in graphical form and discusse

Combined Approach of PNN and Time-Frequency as the Classifier for Power System Transient Problems

The transients in power system cause serious disturbances in the reliability, safety and economy of the system. The transient signals possess the nonstationary characteristics in which the frequency as well as varying time information is compulsory for the analysis. Hence, it is vital, first to detect and classify the type of transient fault and then to mitigate them. This article proposes time-frequency and FFNN (Feedforward Neural Network) approach for the classification of power system transients problems. In this work it is suggested that all the major categories of transients are simulated, de-noised, and decomposed with DWT (Discrete Wavelet) and MRA (Multiresolution Analysis) algorithm and then distinctive features are extracted to get optimal vector as input for training of PNN (Probabilistic Neural Network) classifier. The simulation results of proposed approach prove their simplicity, accurateness and effectiveness for the automatic detection and classification of PST (Power System Transient) type

Estimation of Energy Potential from Organic Fractions of Municipal Solid Waste by Using Empirical Models at Hyderabad, Pakistan.

MSW (Municipal Solid Waste) now-a-day is considered as a precious renewable energy resource for various purposes. In view of above fact, one hundred samples of MSW were collected from different locations of study area. Quantities of each organic waste component were determined by using physical balance and also their proximate analysis was performed by using oven and muffle furnace. In this study, nine empirical models were used for estimating the energy value in terms of heat from OFMSW (Organic Fractions of Municipal Solid Waste), namely two of them were based upon physical composition, four of them were on the basis of its proximate analysis and remaining three of them was according to ultimate analysis of OFMSW. From comparison of all energy models, the empirical Model No. 3 and No. 4 based upon proximate analysis have highest energy recovery potential than all of others. Moreover, the result of Model No.3 on the basis of proximate analysis is closer to the calorific value of mixed OFMSW than the values obtained by rest of models. Therefore, this is the best model to be used. From the outcomes of this study it can be realized that the energy recovery from the OFMSW plays a vital role for economical growth of the country. On that account, a systematic approach should be performed in detail before making a decision on such optio

Gate Driver Circuit of Power Electronic Switches with Reduced Number of Isolated DC/DC Converter for a Switched Reluctance Motor

This paper presents a gate driver circuit for the switching devices used in the asymmetrical converter for a switched reluctance machine with reduced number of isolated dc/dc converters. Isolation required in the gate driver circuit of switching devices is indispensable. For the purpose of isolation different arrangements may be used such as pulse transformers. The dc/dc converter for isolation and powering the gate drive circuits is suitable, cheaper in cost and simple to implement. It is also significant that required number of isolation converters is much less than the switches used in converter. In addition, a simple logic circuit has been presented for producing the gate signals at correct phase sequence which is compared with the gated signals directly obtained from the encoder of an existing machine

Least Square Regression Based Integrated Multi-Parameteric Demand Modeling for Short Term Load Forecasting

Nowadays, due to power crisis, electricity demand forecasting is deemed an important area for socioeconomic development and proper anticipation of the load forecasting is considered essential step towards efficient power system operation, scheduling and planning. In this paper, we present STLF (Short Term Load Forecasting) using multiple regression techniques (i.e. linear, multiple linear, quadratic and exponential) by considering hour by hour load model based on specific targeted day approach with temperature variant parameter. The proposed work forecasts the future load demand correlation with linear and non-linear parameters (i.e. considering temperature in our case) through different regression approaches. The overall load forecasting error is 2.98% which is very much acceptable. From proposed regression techniques, Quadratic Regression technique performs better compared to than other techniques because it can optimally fit broad range of functions and data sets. The work proposed in this paper, will pave a path to effectively forecast the specific day load with multiple variance factors in a way that optimal accuracy can be maintaine

Delayed-Input Wide Area Power System Stabilizer for Mode Selective Damping of Electromechanical Oscillations

A long time delay due to the transmission and processing of remote signal may degrade stability of power system. This paper discusses the design of H? -based local decentralized delayed-input PSS (Power System Stabilizer) controllers for a separate better damping of inter-area modes. The controllers use selected suitable remote signals from whole system as supplementary inputs. The local and remote input signals, used by the controller, are the ones in which the assigned single inter-area mode is most observable. The controller is located at a generator which is most effective in controlling the assigned mode. The controller, designed for a particular single interarea mode, also works mainly in the natural frequency of the assigned mode. Pade approximation approach is used to model time delay. The time delay model is then merged into delay-free power system model to obtain the delayed-input power system model. The controllers are then redesigned for the delayed-input system