A multifunctional dynamic voltage restorer for power quality improvement

Power quality is a major concern in electrical power systems. The power quality disturbances such as sags, swells, harmonic distortion and other interruptions have an impact on the electrical devices and machines and in severe cases can cause serious damages. Therefore it is necessary to recognize and compensate all types of disturbances at an earliest time to ensure normal and efficient operation of the power system. To solve these problems, many types of power devices are used. At the present time, one of those devices, Dynamic Voltage Restorer (DVR) is the most efficient and effective device used in power distribution systems. In this paper, design and modeling of a new structure and a new control method of multifunctional DVRs for voltage quality correction are presented. The new control method was built in the stationary frame by combining Proportional Resonant controllers and Sequence-Decouple Resonant controllers. The performance of the device and this method under different conditions such as voltage swell, voltage sag due to symmetrical and unsymmetrical short circuit, starting of motors, and voltage distortion are described. Simulation result show the superior capability of the proposed DVR to improve power quality under different operating conditions and the effectiveness of the proposed method. The proposed new DVR controller is able to detect the voltage disturbances and control the converter to inject appropriate voltages independently for each phase and compensate to load voltage through three single-phase transformers.Web of Science116art. no. 135

Incremental learning for large-scale stream data and its application to cybersecurity

As many human currently depend on technologies to assist with daily tasks, there are more and more applications which have been developed to be fit in one small gadget such as smart phone and tablet. Thus, by carrying this small gadget alone, most of our tasks are able to be settled efficiently and fast. Until the end of 20th century, mobile phones are only used to call and to send short message service (sms). However, in early 21st century, a rapid revolution of communi�cation technology from mobile phone into smart phone has been seen in which the smart phone is equipped by 4G Internet line along with the telephone service provider line. Thus, the users are able to make a phone call, send messages using variety of application such as Whatsapp and Line, send email, serving websites, accessing maps and handling some daily tasks via online using online banking, online shopping and online meetings via video conferences. In previous years, if there are cases of missing children or missing cars, the victims would rely on the police investigation. But now, as easy as uploading a notification about the loss on Facebook and spread the news among Facebook users, there are more people are able to help in the search. Despite the advantages that can be obtained using these technologies, there are a group of irresponsible people who take advan�tage of current technologies for their own self-interest. Among the applications that are usually being used by almost Internet users and also are often misused by cyber criminals are email and websites. Therefore, we take this initiative to make enhancement in cyber security application to avoid the Internet users from being trapped and deceived by the trick of cyber criminals by developing detec�tion system of malicious spam email and Distributed Denial of Services (DDoS) 377$3(53867$.1781.8781$0,1$+ iii backscatter. Imagine that a notice with a logo of Mobile Phone company is received by an email informing that the customer had recently run up a large mobile phone bill. A link regarding the bill is attached for him/her to find out the details. Since, the customer thinks that the billing might be wrong, thus the link is clicked. However, the link is directed to a webpage which displays a status that currently the webpage is under construction. Then the customer closes the page and thinking of to visit the website again at other time. Unfortunately, after a single click actually a malicious file is downloaded and installed without the customer aware of it. That malicious file most probably is a Trojan that capable to steal confidential information from victim’s computer. On the next day, when the same person is using the same computer to log in the online banking, all of a sudden find out that his/her money is lost totally. This is one of a worst case scenario of malicious spam email which is usually handled by cybersecurity field. Another different case of cybersecurity is the Distributed Denial of Services (DDoS) attack. Let say, Company X is selling flowers via online in which the market is from the local and international customer. The online business of Company X is running normally as usual, until a day before mother’s day, the webpage of Company X is totally down and the prospective customers could not open the webpage to make order to be sent specially for their beloved mother. Thus, the customers would search another company that sells the same item. The Company X server is down, most probably because of the DDoS attack where a junk traffic is sent to that company server which makes that server could not serve the request by the legitimate customers. This attack effect not only the profit of the company, but also reputation damage, regular customer turnover and productivity decline. Unfortunately, it is difficult for a normal user like us to detect malicious spam 377$ 3(53867$.1781.8781$0,1$+ email or DDoS attack with naked eyes. It is because recently the spammers and attacker had improved their strategy so that the malicious email and the DDoS packets are hardly able to be differentiated with the normal email and data packets. Once the Social Engineering is used by the spammers to create relevant email content in the malicious spam email and when a new campaign of DDoS attack is launched by the attacker, no normal users are capable to distinguish the benign and malicious email or data packets. This is where my Ph.D project comes in handy. My Ph.d is focusing on constructing a detection system of malicious spam email and DDoS attack using a large number of dataset which are obtained by a server that collect double-bounce email and darknet for malicious spam email detection system and DDoS backscatter detection system, respectively. As many up-to-date data are used during the learning, the detection system would become more robust to the latest strategy of the cybercriminal. Therefore, the scenario mentioned above can be avoided by assisting the user with important information at the user-end such as malicious spam email filter or at the server firewall. First of all, the method to learn large-scale stream data must be solved before implementing it in the detection system. Therefore, in Chapter 2, the general learning strategy of large-scale data is introduced to be used in the cybersecurity applications which are discussed in Chapter 3 and Chapter 4, respectively. One of a critical criterion of the detection system is capable to learn fast because after the learning, the updated information needs to be passed to user to avoid the user from being deceived by the cybercriminal. To process large-scale data sequences, it is important to choose a suitable learning algorithm that is capable to learn in real time. Incremental learning has an ability to process large data in chunk and update the parameters after learning each chunk. Such type of learning keep and update only the minimum information on a classifier model. 377$3(53867$.1781.8781$0,1$+ Therefore, it requires relatively small memory and short learning time. On the other hand, batch learning is not suitable because it needs to store all training data, which consume a large memory capacity. Due to the limited memory, it is certainly impossible to process online large-scale data sequences using the batch learning. Therefore, the learning of large-scale stream data should be conducted incrementally. This dissertation contains of five chapters. In Chapter 1, the concept of in�cremental learning is briefly described and basic theories on Resource Allocating Network (RAN) and conventional data selection method are discussed in this chapter. Besides that, the overview of this dissertation is also elaborated in this chapter. In Chapter 2, we propose a new algorithm based on incremental Radial Basis Function Network (RBFN) to accelerate the learning in stream data. The data sequences are represented as a large chunk size of data given continuously within a short time. In order to learn such data, the learning should be carried out incrementally. Since it is certainly impossible to learn all data in a short pe�riod, selecting essential data from a given chunk can shorten the learning time. In our method, we select data that are located in untrained or “not well-learned” region and discard data at trained or “well-learned” region. These regions are represented by margin flag. Each region is consisted of similar data which are near to each other. To search the similar data, the well-known LSH method pro�posed by Andoni et al. is used. The LSH method indeed has proven be able to quickly find similar objects in a large database. Moreover, we utilize the LSH ʼs properties; hash value and Hash Table to further reduced the processing time. A flag as a criterion to decide whether to choose or not the training data is added in the Hash Table and is updated in each chunk sequence. Whereas, the hash value of RBF bases that is identical with the hash value of the training data is used to select the RBF bases that is near to the training data. The performance results of 377$ 3(53867$.1781.8781$0,1$+ vi the numerical simulation on nine UC Irvine (UCI) Machine Learning Repository datasets indicate that the proposed method can reduce the learning time, while keeping the similar accuracy rate to the conventional method. These results indi�cate that the proposed method can improve the RAN learning algorithm towards the large-scale stream data processing. In Chapter 3, we propose a new online system to detect malicious spam emails and to adapt to the changes of malicious URLs in the body of spam emails by updating the system daily. For this purpose, we develop an autonomous system that learns from double-bounce emails collected at a mail server. To adapt to new malicious campaigns, only new types of spam emails are learned by introducing an active learning scheme into a classifier model. Here, we adopt Resource Allocating Network with Locality Sensitive Hashing (RAN-LSH) as a classifier model with data selection. In this data selection, the same or similar spam emails that have already been learned are quickly searched for a hash table using Locally Sensitive Hashing, and such spam emails are discarded without learning. On the other hand, malicious spam emails are sometimes drastically changed along with a new arrival of malicious campaign. In this case, it is not appropriate to classify such spam emails into malicious or benign by a classifier. It should be analyzed by using a more reliable method such as a malware analyzer. In order to find new types of spam emails, an outlier detection mechanism is implemented in RAN-LSH. To analyze email contents, we adopt the Bag-of-Words (BoW) approach and generate feature vectors whose attributes are transformed based on the normalized term frequency-inverse document frequency. To evaluate the developed system, we use a dataset of double-bounce spam emails which are collected from March 1, 2013 to May 10, 2013. In the experiment, we study the effect of introducing the outlier detection in RAN-LSH. As a result, by introducing the outlier detection, we confirm that the detection accuracy is enhanced on 377$3(53867$.$$1781.8781$0,1$+ average over the testing period. In Chapter 4, we propose a fast Distributed Denial of Service (DDoS) backscat�ter detection system to detect DDoS backscatter from a combination of protocols and ports other than the following two labeled packets: Transmission Control Protocol (TCP) Port 80 (80/TCP) and User datagram Protocol (UDP) Port 53 (53/UDP). Usually, it is hard to detect DDoS backscatter from the unlabeled packets, where an expert is needed to analyze every packet manually. Since it is a costly approach, we propose a detection system using Resource Allocating Network (RAN) with data selection to select essential data. Using this method, the learning time is shorten, and thus, the DDoS backscatter can be detected fast. This detection system consists of two modules which are pre-processing and classifier. With the former module, the packets information are transformed into 17 feature-vectors. With the latter module, the RAN-LSH classifier is used, where only data located at untrained region are selected. The performance of the proposed detection system is evaluated using 9,968 training data from 80/TCP and 53/UDP, whereas 5,933 test data are from unlabeled packets which are col�lected from January 1st, 2013 until January 20th, 2014 at National Institute of Information and Communications Technology (NICT), Japan. The results indi�cate that detection system can detect the DDoS backscatter from both labeled and unlabeled packets with high recall and precision rate within a short time. Finally, in Chapter 5, we discussed the conclusions and the future work of our study: RAN-LSH classifier, malicious spam email detection system and DDoS backscatter detection system

Power Quality Improvement and Low Voltage Ride through Capability in Hybrid Wind-PV Farms Grid-Connected Using Dynamic Voltage Restorer

© 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission.This paper proposes the application of a dynamic voltage restorer (DVR) to enhance the power quality and improve the low voltage ride through (LVRT) capability of a three-phase medium-voltage network connected to a hybrid distribution generation system. In this system, the photovoltaic (PV) plant and the wind turbine generator (WTG) are connected to the same point of common coupling (PCC) with a sensitive load. The WTG consists of a DFIG generator connected to the network via a step-up transformer. The PV system is connected to the PCC via a two-stage energy conversion (dc-dc converter and dc-ac inverter). This topology allows, first, the extraction of maximum power based on the incremental inductance technique. Second, it allows the connection of the PV system to the public grid through a step-up transformer. In addition, the DVR based on fuzzy logic controller is connected to the same PCC. Different fault condition scenarios are tested for improving the efficiency and the quality of the power supply and compliance with the requirements of the LVRT grid code. The results of the LVRT capability, voltage stability, active power, reactive power, injected current, and dc link voltage, speed of turbine, and power factor at the PCC are presented with and without the contribution of the DVR system.Peer reviewe

Advanced Control of the dynamic voltage restorer for mitigating voltage sags in power systems

The paper presents a vector control with two cascaded loops to improve the properties of Dynamic Voltage Restorer (DVR) to minimize Voltage Sags on the grid. Thereby, a vector controlled structure was built on the rotating dq-coordinate system with the combination of voltage control and the current control. The proposed DVR control method is modelled using MATLAB-Simulink. It is tested using balanced/ unbalanced voltage sags as well as fluctuant and distorted voltages. As a result, by using this controlling method, the dynamic characteristics of the system have been improved significantly. The system performed with higher accuracy, faster response and lower distortion in the voltage sags compensation. The paper presents real time experimental results to verify the performance of the proposed method in real environments

Continuous biodiesel process using ultrasonic in-line reactor for Jatropha Curcas Oil (JCO)

Biodiesel is an alternative fuel for replacing diesel fuel in compression ignition engines. Due to the complexity of the diesel fuel production and exhaust emissions from petroleum-fuelled engines will give negative impact to the environment. In this study, the sodium hydroxide as the catalyst was used to react with methanol for obtaining chemical compound that is called methyl ester which is known as biodiesel. The method used are Ultrasonic. Basically, this method will reduce the reaction time on the conversation of jatropha curcas oil (JCO) into biodiesel. The experiment was to determine the effect of esters contents by reaction time, molar ratio methanol (MeOH) to JCO, the amount of catalyst, frequency and power output of ultrasonic using ultrasonic in-line reactor. The optimum production of biodiesel was achieved at 7 minutes of reaction time, 1%wt of catalyst concentration and molar ratio methanol to oil 12:1, frequency ultrasonic of 20 KHz and ultrasonic output 600 Watt at temperature 65°C. The biodiesel produced by this method has been referred according to ASTM D6751. From the result, the biodiesel produced from this method has satisfied the requirement biodiesel standard. This optimum result in this research can be used to run larger pilot plant designed for industry

Dynamic Voltage Restorer for Mitigation of Voltage Sags Due to 3 Phase Motor Starts Based on Artificial Neural Networks

The Direct On-Line (DOL) process of starting a high-power 3-phase induction motor causes voltage sags in the distribution system that is connected to one point of common coupling (PCC). Voltage sag can cause damage and failure of sensitive loads. This article analyzes and proposes a simulation of voltage sag recovery using a Dynamic Voltage Restorer (DVR) based on an Artificial Neural Network (ANN). ANN is used as a detector and regulator of the voltage compensation value. In this study, a 3-phase induction motor will be connected to a sensitive load, and the DVR will be placed in series with a voltage source or PCC with a sensitive load. The simulation test system uses Simulink-Matlab R2016a with different configurations of induction motor parameters. Based on the simulation results show that the parameters of the 3-phase induction motor cause the depth and duration of the voltage sag. DVR with ANN control can detect and compensate for a voltage sag of 0.5 pu so that the voltage will be normal to 1 pu

Dynamic Voltage Restorer Application for Power Quality Improvement in Electrical Distribution System: An Overview

Dynamic Voltage Restorer (DVR) is a custom power device that is used to improve voltage disturbances in electrical distribution system. The components of the DVR consist of voltage source inverter (VSI), injection transformers, passive filters and energy storage. The main function of the DVR is used to inject three phase voltage in series and in synchronism with the grid voltages in order to compensate voltage disturbances. The Development of (DVR) has been proposed by many researchers. This paper presents a review of the researches on the DVR application for power quality Improvement in electrical distribution network. The types of DVR control strategies and its configuration has been discussed and may assist the researchers in this area to develop and proposed their new idea in order to build the prototype and controller

UKF based estimation approach for DVR control to compensate voltage swell in distribution systems

AbstractThe Dynamic Voltage Restorer (DVR) is identified as the best solution for mitigation of voltage sag and swell related problems in the much taped distribution system. The compensation performance of the DVR very much depends on its control algorithm. In the paper, an estimation method based on Unscented Kalman Filter (UKF) is proposed for mitigating the voltage swell concern. The proposed UKF based estimation technique is used to assist the control algorithm for generating reference signals of Voltage Source Converter (VSC) of DVR. DVR presents the compensation voltage as output which is included in the connected line. With this estimation method, voltage swell issues are discovered with accuracy and faster performance to retract out the swell problem in sensitivity load linked distribution systems. In MATLAB/Simulink platform the suggested method is executed and its performance is assessed and contrasted with the Linear Kalman Filter (LKF) and Extended Kalman Filter (EKF)

Voltage Dips and Mitigation System using Dynamic Voltage Restorer

Quality of the output power delivered has become a major concern for heavy duty industry for present era. The poor power quality is due to some disturbances such as voltage sags/dips, surge, flicker, harmonics, imbalance and interruptions. It becomes more important especially with the sophisticated devices whose performance is very useful to the quality of power supply. Power quality problem in industries, factories, university, residential areas etc cause failure to the equipment and malfunction which can make the operating system stop and at the end gives a big impact to the plant production. These issues become worse if there is no mitigation system to counter back the problem. A voltage dip is loss of rms voltage which is a major power quality problem. In specific term of duration and retained voltage, it can also be representing as the lowest rms remaining voltage at the lowest point during the dips at the industry. So, it is the responsibility of the user and customers to identify the root cause of the problem and create a mitigation system to overcome the problem. To overcome this abnormal problem custom power devices are connected closer to the end of the load. One of the devices is the Dynamic Voltage Restorer (DVR) which is the most efficient custom power device used in power distribution networks. This paper presents modeling, simulation, and analysis of a Dynamic Voltage Restorer (DVR) using PSCAD (Power System Computer Aided Design) software. The main component of a DVR is voltage source converter (VSI) which is operated by pulse width modulation (PWM) technique and a PI controller. There are many different type of fault has been created and shown reliable result after mitigate usingDV