Recent Advances in Electrical & Electronic Engineering - Volume 13, Issue 7, 2020

An extensive use of fossil fuel has led to an extreme increment of carbon footprint in the world globally. Glaciers are melting on the earth’s poles due to the greenhouse effect. Many such natural indications urge us to adopt renewable energy sources. In most cases, renewable power is generated in electrical form, which does not suit the existing grid requirements. Cascaded H bridge with multilevel inverter topology is promising in this context. The output of the multilevel inverter is near to sinusoidal. It has added advantages of low device stress, no need for step-up transformers, low common-mode voltage, near sinusoidal input current, low switching frequency, and reduced harmonics. The direct output of Cascaded H bridge multilevel inverters may not be suitable for many applications like integrating solar panel output with an existing grid where harmonic reduction is necessary. Various modulation techniques are available like sinusoidal pulse width modulation, space vector modulation, and selective harmonic elimination. Among the listed modulation techniques, selective harmonic elimination can be implemented with the low switching frequency, and it is suitable where low electromagnetic interference, low switching loss, and good power quality are required. To reduce the harmonics, one must solve the non-linear transcendental output equations of the cascaded H-bridge Multilevel inverter (CHB-MLI). Various Artificial Intelligent (AI) algorithms are introduced, and researchers have worked on eliminating harmonics or minimizing Total Harmonics Distortion (THD) from the output of CHB-MLI. This paper gives a review of the harmonic reduction using some of the well-known explored AI algorithms. It also provides insight into some of the unexplored algorithms in this area.

Background: Over the past few years, the subsynchronous oscillation (SSO) caused by the grid-connected wind farm had a bad influence on the stable operation of the system and has now become a bottleneck factor restricting the efficient utilization of wind power. How to mitigate and suppress the phenomenon of SSO of wind farms has become the focus of power system research. Methods: This paper first analyzes the SSO of different types of wind turbines, including squirrelcage induction generator based wind turbine (SCIG-WT), permanent magnet synchronous generator- based wind turbine (PMSG-WT), and doubly-fed induction generator based wind turbine (DFIG-WT). Then, the mechanisms of different types of SSO are proposed with the aim to better understand SSO in large-scale wind integrated power systems, and the main analytical methods suitable for studying the SSO of wind farms are summarized. Results: On the basis of results, using additional damping control suppression methods to solve SSO caused by the flexible power transmission devices and the wind turbine converter is recommended. Conclusion: The current development direction of the SSO of large-scale wind farm grid-connected systems is summarized and the current challenges and recommendations for future research and development are discussed.

Background: Short Term Load Forecasting (STLF) can predict load from several minutes to week plays a vital role to address challenges such as optimal generation, economic scheduling, dispatching and contingency analysis. Methods: This paper uses Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN) technique to perform STFL but long training time and convergence issues caused by bias, variance and less generalization ability, make this algorithm unable to accurately predict future loads. Results: This issue can be resolved by various methods of Bootstraps Aggregating (Bagging) (like disjoint partitions, small bags, replica small bags and disjoint bags) which help in reducing variance and increasing generalization ability of ANN. Moreover, it results in reducing error in the learning process of ANN. Disjoint partition proves to be the most accurate Bagging method and combining outputs of this method by taking mean improves the overall performance. Conclusion: This method of combining several predictors known as Ensemble Artificial Neural Network (EANN) outperforms the ANN and Bagging method by further increasing the generalization ability and STLF accuracy.

Background: The Internet of Thing is a network that enables multiple hardware devices, sensors and other home applications from electronically communicate with each other. New era such technology is increasing importance mainly due to the revolutionary development of information technologies. Methods: However, energy efficiency is still a big challenge facing IoT technology. Thus, it becomes an interesting topic for many researchers to investigate. Current work aims to reduce the energy consumption thereby introducing the shortest path technique and another new practicing for Particle Swarm Optimization algorithm in the Internet of Thing cooperative clusters. Results: The main concept is based on cluster heads cooperation with each other known as Cooperative Clusters to transfer information to the base station. The Primary results reveals a 17% and 16% reduction in energy consumption was achieved over the shortest path technique and Particle Swarm Optimization algorithm respectively. Results also show a remarkable improvement in the system lifetime due to the new applied scheme. Conclusion: The other method is by PSO algothrim at beginig it sending Randomly then it will select the path by using the feed back acknowledgment after that it will collect the information by sending it for the Cluster heads by updating the information status automatically. That’s why we discovered the PSO advantages than the others.

Background: In the existing inverse system decoupling methods of bearingless induction motor, the inverse system model is more complex, and it is not easy to realize the independent control of the magnetic suspension system. In this paper, in order to simplify its inverse system model, an independent inverse system decoupling control strategy is proposed. Methods: Under the conditions of considering the current dynamics of torque windings, the state equations of torque system and those of magnetic suspension system are established, and the independent inverse system model of torque system and that of the magnetic suspension system are deduced. The air gap fluxlinkage of the torque system that is needed in the magnetic suspension system is identified by an independent voltage model. After the independent inverse model of torque system and that of magnetic suspension system are connected in parallel, they are connected in front of the original system of a bearingless induction motor. After this, the torque system is decoupled into two second-order integral subsystems, i.e. a fluxlinkage subsystem and a motor speed subsystem, while the magnetic suspension system is decoupled into another two second-order integral subsystems, i.e. the α- and β-displacement component subsystems. The design of the additional closed-loop controller is achieved through the pole assignment method. Results: The obtained inverse model of the magnetic suspension system is independent of the fluxlinkage orientation mode of torque system, and thus the flexibility of the independent control for the torque system and magnetic suspension system is increased. The simulation results have shown that the system has good static- and dynamic-decoupling control performance. Conclusion: The proposed independent inverse system decoupling control strategy is effective and feasible.

Background: This paper presents voltage and current quality improvement in high/medium electrical distribution system using modulated multilevel unified power quality conditioner (MM-UPQC). Nowadays, power quality is one of the major issues due to the increase in usage of more non-linear loads in agricultural, commercial, industrial sectors. The industrial loads produce large amount of harmonics and power imbalances, which cause various power quality related issues like poor power factor, voltage sag, voltage swell, voltage interruption etc. Methods: The prime objective of this work is to design fuzzy-PI based controller based modulated multilevel UPQC for mitigation of issues related to power quality under unsymmetrical fault conditions such as LG fault and LLG fault. Results: This paper uses Instantaneous Reactive Power Theory (IRP) for phase angle adjustment with PI-fuzzy controller scheme to generate accurate reference signal for shunt and series controller of MM-UPQC. The detailed comparative analysis results of simultaneous voltage sag, swell, harmonics compensation and unsymmetrical faults mitigation are presented alongwith the MATLAB/SIMULINK software. Conclusion: Total harmonic distortion analysis is tabulated with PI and fuzzy-PI controller based MM-UPQC for different operating conditions in 4.16 KV distribution system.

Background: Multi-Input Multi-Output (MIMO) systems using Orthogonal Space-Time Block Coding (OSTBC) gained extensive popularity in wireless applications owing to the potential of providing improved reliability. Methods: The performance of MIMO - OSTBC systems using error-correcting code (Convolutional, Reed Solomon and Interleaving) schemes used to encode data streams in wireless communications using the Rayleigh channel is reported here. These are subjected to experimentation under modulation schemes such as Quadrature Phase Shift Keying (QPSK). Decoding occurs using the Maximum Likelihood (ML) algorithm, which provides high data rates using spatial domains under the limits of power transmission and limited bandwidths. Results: Different simulations are performed to detect the best BER performance for various antenna configurations and values of antenna configurations with Error Correction so as to use the best outcomes to model the OSTBC. Conclusion: Their effect of improving the overall can be noticed by the advantages of OSTBC with the correcting codes and the maximum number of configurations.

Background: The scope of this paper is to study the energy trading in microgrids. Microgrids are low voltage or medium voltage distribution networks, which consist of energy storage systems, electric loads, e.g. electric vehicles and Renewable Energy Sources (RES). Methods: Legacy energy grids are being transformed by the introduction of small to medium sized individual or cooperative, mostly RES invested energy producers and prosumers. Electric vehicles penetrate the market and modern power grids integrate them as ancillary services providers when there are peak domestic loads, as well as in order to balance grid voltage aiming to increase system reliability, compensating for renewable energy sources’ intermittency and volatility in energy production. Results: An elaborate management algorithm is proposed in this paper, to balance demand and local renewable energy sources microgrid supply. Conclusion: Finally, the results of simulations of different scenarios, including economic parameters and proposals for future research are presented.

Background: Low power factor is regarded as one of the most dedicated issues in large scale inductive power networks, because of the lost energy in term of a reactive power. Accordingly, installing capacitors in the network improves the power factor and hence decreases the reactive power. Methods: This paper presents an approach to maximize the saving in terms of financial costs, energy resources, environmental protection, and also enhance the power system efficiency. Moreover, the proposed technique tends to avoid the penalties imposed over the electricity bill (in the case of the power factor drops below the permissible limit), by applying a proposed method that consists of two stages. The first stage determines the optimal amount of compensating capacitors by using a suggested analytical method. The second stage employs a statistical approach to assess the reduction in energy losses resulting from the capacitors placement in each of the network nodes. Accordingly, the expected beneficiaries from improving the power factor are mainly large inductive networks such as large scale factories and industrial field. A numerical example is explained in useful detail to show the effectiveness and simplicity of the proposed approach and how it works. Results: The proposed technique tends to minimize the energy losses resulted from the reactive power compensation, release the penalties imposed on electricity bills due to the low power factor. The numerical examples show that the saved cost resulted from improving the power factor, and energy loss reduction is around 10.94 % per month from the total electricity bill. Conclusion: The proposed technique to install capacitors has significant benefits and effective power consumption improvement when the cost of the imposed penalty is regarded as high. The tradeoff in this technique is between the cost of the installed capacitors and the saving gained from the compensation.

Background: With the tremendous changes in the world’s fuel structure, the Electric Vehicle (EV) has become a powerful means of mitigating energy and environmental issues. Objective: However, when an electric vehicle is connected to home, it will cause load fluctuation, which threatens the safe and smooth operation of the user's electricity. Methods: Therefore, in order to solve the problem of power instability when the electric vehicle is connected to home, this paper proposes an optimization strategy for household charging based on Smart Load (SL). Results: After the daily load fluctuation model of electric vehicle family charging is constructed, the Particle Swarm Optimization (PSO) algorithm is combined to establish the electric vehicle family charging optimization model. Conclusion: The analysis of the example shows that the proposed method can stabilize the household power, which can effectively solve the adverse effects caused by excessive fluctuation of daily load in the family.

Background: In order to better establish accurate load models and meet the practical demand of current power system load modeling, it is necessary to establish related technical support systems for power system load modeling. Objective: The purpose of the paper was to construct the overall scheme of power system load modeling technology support system and complete the development of the system. Methods: Based on the modular design idea, the system adopts a multi-level architecture combining B/S and C/S modes, covering the key technologies of substation classification based on selforganizing neural network algorithm, load dynamic characteristic classification based on lifting wavelet packet algorithm, load model parameter identification and load modeling based on adaptive interactive multiple model (AIMM) algorithm. Results: After actual operation verification, the built technology support system can well solve the related problems of substation classification, load dynamic characteristic classification, load model parameter identification and load modeling. It has the characteristics of a friendly man-machine interface, simple operation and strong extensibility. Conclusion: The built technology support system provides powerful technical support for improving the load data management level of the power system and establishing an accurate load model, and promotes the practical process of load modeling theory.

Background: Although correlation filtering is one of the most successful visual tracking frameworks, it is prone to drift caused by several factors such as occlusion, deformation and rotation. Objective: In order to improve the performance of correlation filter-based trackers, this paper proposes a visual tracking method via online reliability evaluation and feature selection. Methods: The main contribution of this paper is to introduce three schemes in the framework of correlation filtering. Firstly, we present an online reliability evaluation to assess the current tracking result by using the method of adaptive threshold segmentation of response map. Secondly, the proposed tracker updates the regression model of correlation filter according to the assessment result. Thirdly, when the tracking result based on a handcrafted feature is not reliable enough, we propose a feature selection scheme that autonomously replaces a handcrafted feature used in the traditional correlation filter-based trackers with a deep convolutional feature that can re-capture the target by its powerful discriminant ability. Results: On OTB-2013datasets, the Precision rate and Success rate of the proposed tracking algorithm can reach 84.8% and 62.5%, respectively. Moreover, the tracking speed of proposed algorithm is 19 frame per second. Conclusion: The quantitative and qualitative experimental results both demonstrate that the proposed algorithm performed favorably against nine state-of-the-art algorithms.

Background: The penetration level of a wind farm with transient stability constraint and static security constraint has been a key problem in wind power applications. Objective: The study explores maximum penetration level problem of wind considering transient stability constraint and uncertainty of wind power out, based on credibility theory and corrected energy function method. Methods: According to the corrected energy function, the transient stability constraint of the power grid is transferred to the penetration level problem of a wind farm. Wind speed forecast error is handled as a fuzzy variable to express the uncertainty of wind farm output. Then this paper builds a fuzzy chance-constrained model to calculate wind farm penetration level. To avoid inefficient fuzzy simulation, the model is simplified to a mixed integer linear programming model. Results: The results validate the proposed model and investigate the influence of grid-connection node, wind turbine characteristic, fuzzy reliability index, and transient stability index on wind farm penetration level. Conclusion: The result shows that the model proposed in this study can consider the uncertainty of wind power out and establish a quantitative transient stability constraint to determine the wind farm penetration level with a certain fuzzy confidence level.

Background: The high permeability of Distributed Generation (DG) and the development of DC load represented by electric vehicle Battery Swapping Station (BSS) pose new challenges to the reliable and economic operation of DC distribution system. Methods: In order to improve the wind and solar absorption rate and the reliable operation of DC distribution network and coordinate the interests and demands of BSS and DC distribution company, the upper level takes the abandonment rate and the minimum variance of BSS charging and discharging net load as two objective functions, and the lower level takes the minimum operation cost of DC distribution network and BSS as the objective function. Secondly, this paper proposes a method that combines Genetic Algorithm (GA) with Wind-Driven Optimization algorithm (WDO). CPLEX and hybrid GA-WDO are used to solve the upper and lower models, respectively. Results: Finally, an example shows that the proposed optimization model can reduce the operation cost of DC distribution network with BSS and improve the utilization rate of wind and light, which shows the rationality and effectiveness of the optimization model. Conclusion: In this paper, considering the randomness and uncertainty of wind power generation and photovoltaic power generation, this paper establishes the upper objective function with the minimum abandonment rate and load variance and the lower objective function with the minimum operation cost of DC distribution network and BSS operators.

Background: Loop closure detection is a crucial part in robot navigation and simultaneous location and mapping (SLAM). Appearance-based loop closure detection still faces many challenges, such as illumination changes, perceptual aliasing and increasing computational complexity. Methods: In this paper, we proposed a visual loop closure detection algorithm that combines illumination robust descriptor DIRD and odometry information. In this algorithm, a new distance function is built by fusing the Euclidean distance function and Mahalanobis distance function, which integrates the pose uncertainty of body and can dynamically adjust the threshold of potential loop closure locations. Then, potential locations are verified by calculating the similarity of DIRD descriptors. Results: The proposed algorithm is evaluated on KITTI and EuRoC datasets, and is compared with SeqSLAM algorithm, which is one of the state of the art loop closure detection algorithms. The results show that the proposed algorithm could effectively reduce the computing time and get better performance on P-R curve. Conclusion: The new loop closure detection method makes full use of odometry information and image appearance information. The application of the new distance function can effectively reduce the missed detection caused by odometry error accumulation. The algorithm does not require extracting image features or learning stage, and can realize real-time detection and run on the platform with limited computational power.