Power System Transmission Line Research Articles

A Review of Energy Harvesting Methods for Power Transmission Line Monitoring Sensors

Condition monitoring sensors have the responsibility of reducing occupational failures or unscheduled shutdowns especially in power transmission line systems. Existing sensors that are used for condition monitoring are mostly battery-dependent. Powering up these sensors in difficult to access areas where high voltage transmission line usually runs is a challenge because batteries usually have a limited life cycle. Power sources other than batteries such as harvesting from solar energy, magnetic energy, radio frequency energy either produces insufficient energy or not entirely available all the time. Electric Field Energy Harvesting (EFEH) overcomes many of these disadvantages and provides a quality and continuous power source to be used to power up devices especially the monitoring sensors that are used in transmission line monitoring. This paper presents key aspects and drawbacks of six types of energy harvesting methods and a review of existing energy harvesters. The concept of electric field and the usage of EFEH in power transmission line system are explained and a comparison between EFEH with typical energy harvesting methods is discussed. This paper finds that EFEH devices have potential to provide sufficient energy for low powered condition monitoring sensors. Moreover, several improved EFEH approaches are proposed, and future trends are discussed.

Complex Power System Status Monitoring and Evaluation Using Big Data Platform and Machine Learning Algorithms: A Review and a Case Study

Efficient and valuable strategies provided by large amount of available data are urgently needed for a sustainable electricity system that includes smart grid technologies and very complex power system situations. Big Data technologies including Big Data management and utilization based on increasingly collected data from every component of the power grid are crucial for the successful deployment and monitoring of the system. This paper reviews the key technologies of Big Data management and intelligent machine learning methods for complex power systems. Based on a comprehensive study of power system and Big Data, several challenges are summarized to unlock the potential of Big Data technology in the application of smart grid. This paper proposed a modified and optimized structure of the Big Data processing platform according to the power data sources and different structures. Numerous open-sourced Big Data analytical tools and software are integrated as modules of the analytic engine, and self-developed advanced algorithms are also designed. The proposed framework comprises a data interface, a Big Data management, analytic engine as well as the applications, and display module. To fully investigate the proposed structure, three major applications are introduced: development of power grid topology and parallel computing using CIM files, high-efficiency load-shedding calculation, and power system transmission line tripping analysis using 3D visualization. The real-system cases demonstrate the effectiveness and great potential of the Big Data platform; therefore, data resources can achieve their full potential value for strategies and decision-making for smart grid. The proposed platform can provide a technical solution to the multidisciplinary cooperation of Big Data technology and smart grid monitoring.

Overhead transmission line outage rate estimation under wind storms

Wind storms such as hurricanes increase the outage rate of overhead transmission lines and hence can seriously affect the security of power systems. Therefore, it is very important to estimate the overhead transmission line outage rate for a power system that might be jeopardized by wind storms. In this paper, we propose a new method for estimating the outage rate of overhead transmission lines, with two primary advantages. First, the proposed method describes the relationship between the outage rate and the weather factors by a fragility curve customized for overhead transmission lines to ensure that the relationship is precisely represented. Second, the proposed method takes the conductor strength reduction and different physical properties of overhead transmission lines into consideration to increase outage rate estimation accuracy. We test the proposed method by estimating the outage rate of overhead transmission lines in a regional power system based on both historical and simulated wind storms. The test results show that the proposed method generates a more accurate estimation than the conventional approaches. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Origin and mitigation of increased electric fields at high voltage transmission line conductors

In this paper analysis of the origin and mitigation measures of increased electric field at high voltage transmission lines in the electric power system of Bosnia and Herzegovina is given. In the analyzed period (2010–2017) in the electric power system of Bosnia and Herzegovina increased values of power frequency voltages was registered. The performed analysis shows that the increased voltages in the 400 kV nodes of the electric power system of Bosnia and Herzegovina are occurring during the night, mainly in the minimum consumption regimes. Increased voltages can occur throughout the year, but are most frequent during the spring and summer months. In the coming years, four 400 kV interconnection transmission lines to Serbia and Croatia will be built. Increasing of the voltage value causes the formation of the corona as well as energy losses. Taking into account the values of the energy losses due to the corona, it is important to determine the values of the electric field on the surface of the conductors and their immediate vicinity as well as the value of the corona onset electric field in order to know their values in the electric power system and to explore ways to mitigation them from the aspect of maintenance and management costs.

Multi-objective transmission congestion management considering demand response programs and generation rescheduling

With the implementation of restructuring and deregulation in the electricity industry, efficient and economic operations of power systems have become remarkably significant. Meanwhile, congestion in transmission lines is one of the major problems in power system operation. Therefore, in this paper, the multi-objective particle swarm optimization (MOPSO) method has been used for transmission congestion management considering demand response programs (DRPs) and generation rescheduling. Total operation/DR cost, emission and increasing the loading of transmission lines are the objective functions of this problem. Using DRPs increases the operator power of choice with regard to the participation of small consumers in reducing the demand. The method already mentioned has been tested on two test systems (IEEE 30-bus and IEEE 118-bus test systems). The results of the evaluation in the form of different scenarios show that DRPs reduce the power system transmission lines congestion, allowing the use of the transmission lines with less loading capacity. Also, in some instances, without using DRPs, solving the transmission congestion management problem is impossible.

Identification and Prevention of Cascading Failures in Autonomous Microgrid

This paper presents a new method to model and deal with the cascading failures in an autonomous microgrid (MG) in order to protect it against risks that may lead to its complete collapse. Although cascading failure is a term mainly associated with power system networks and transmission lines, its extension to MGs as the future structure of power systems is proposed in this paper. An MG operating as an independent entity in autonomous mode is prone to catastrophic failures due to its limited supply resources and power flow paths; therefore, a blackout model based on ac power flow is extracted that is tailored to these specifications. Based on the associated risks presented by the model for any configuration in advance of the anticipated events, remedial actions are proposed to mitigate the vulnerability of the MG by reconfiguration to alleviate the loadings of the heavily loaded lines. A genetic algorithm is used to find the optimum MG's configuration with less vulnerable lines. Critical loading is also determined for each configuration in order to indicate the necessity of taking remedial actions upon approaching to this level. The effectiveness of the proposed method is investigated by using the IEEE 33 bus sample power network.

Flower pollination algorithm based multi-objective congestion management considering optimal capacities of distributed generations

Transmission Congestion creates hindrance that limit the most economical supply to reach demands. Hence, it is relieved at the earliest to make optimum utilization of available transmission network in order to achieve maximum profits. In this work, optimal capacities of distributed generation (DG) units are inserted to remove the congestion in the transmission lines of bulk power system. Multi-objectives like real power losses, investment costs, voltage deviations and line capacities are converted into single objective and is minimized to obtain the optimal capacities of the DG units. Flower Pollination Algorithm (FPA) is implemented to achieve the best capacities of the DGs that are operating at unity (UPF) and 0.9 lagging power factors. The capacities of DGs are obtained at multiple locations instead of single optimal or sub-optimal location in order to improve the practical feasibility while connecting the DGs. The proposed methodology is practiced on IEEE 30 and 118 bus system to check the effectiveness. Further, the result obtained by FPA are compared with Genetic algorithm (GA) and Particle Swarm Optimization (PSO) approaches in terms of real power losses (RPL) and line flows. Results conveyed that the proposed algorithm had superior features, stable convergence characteristics and good computational efficiency.

Economic Extension of Transmission Line in Deregulated Power System for Congestion Management

<p>The chances of congestion in deregulated market are quite high as compared to the monopolistic market as the customers would like to purchase electricity from cheapest available sources. Ways for Congestion management are: Generation rescheduling, Interruptible load curtailment, Transmission system expansion etc. Transmission expansion planning is the last way for congestion management if congestion is not managed by Generation rescheduling and Interruptible load curtailment. A high mean of Locational marginal price (LMP) at a bus indicates no access to cheap generation and a low mean of LMP indicates access to excess cheap generation and no access to enough loads. Hence, constructing a new line between two buses with low and high mean of LMP will allow the dispatch of the excess cheap generation and flow of energy from low LMP bus to high LMP bus due to price potential difference. Consequently, the flatness of price profile is improved. The final plan is selected by economic analysis.</p><p><strong>Journal of Advanced College of Engineering and Management</strong>, Vol. 3, 2017, Page: 49-54</p>

UPFC based transmission line power load flow control under different operating methods

In this paper the methods to control operation of shunt and series inverters using power flow controller (UPFC) is analyzed with real power flow in power system transmission line. These operation mode using UPFC are executed to reduce Swaying in the control framework transport. The execution about every system may be resolved toward the predefined administrative control. Those arrangement What's more shunt converter working under four separate blending from claiming working modes may be used to ponder An example force framework organize. The line flow in MW oscillation damping is predicated with various modes of operations and verified same with MATLAB/ SIMULINK.

Elimination of Chaotic Ferroresonant Oscillations Originated from TCSC in the Capacitor Voltage Transformer

ABSTRACTFerroresonance is one of the complex phenomena, which occurs in power network. This can create thermal and insulation problems in the power system equipments. It can also cause chaotic oscillations in the power network. The magnitude of the power system capacitor is one of the important factors that lead to ferroresonance in capacitor voltage transformers (CVTs).Thyristor controlled series capacitors (TCSCs) are applied for series compensation in the transmission line of power system. In normal operation, these equipments have capacitance magnitude up to three times of fixed capacitor such that the increased value of transmission line capacitor, which is one of the reasons for ferroresonance occurrence, is provided. This paper studies and investigates the effects of TCSCs on the ferroresonance in the CVTs. Because of nonlinear dynamics of ferroresonance, bifurcation theory is utilized in this paper. Using this theory, variation of system parameters that cause chaotic ferroresonant oscillations can be efficiently analyzed. By using bifurcation and phase plane diagrams, behavioral variations of the system with TCSC can be easily reviewed in this case. The results show that TCSCs raise ferroresonance occurrence in CVT. Since thermal and insulation problems may happen during the occurrence of ferroresonance in CVT and TCSC, a device is used to limit and remove these chaotic oscillations; literally called ferroresonance limiter. Obtained results strongly and efficiently show effectiveness of using this method for restricting ferroresonant oscillations.

A New Approach for Classification of Fault in Transmission Line with Combination of Wavelet Multi Resolution Analysis and Neural Networks

An appropriate fault detection and classification of power system transmission line using discrete wavelet transform and artificial neural networks is performed in this paper. The analysis is carried out by applying discrete wavelet transform for obtained fault phase currents. The work represented in this paper are mainly concentrated on classification of fault and this classification is done based on the obtained energy values after applying discrete wavelet transform by taking this values as an input for the neural network. The proposed system and analysis is carried out in Matlab Simulink.

A New Approach for Classification of Fault in Transmission Line with Combination of Wavelet Multi Resolution Analysis and Neural Networks

An appropriate fault detection and classification of power system transmission line using discrete wavelet transform and artificial neural networks is performed in this paper. The analysis is carried out by applying discrete wavelet transform for obtained fault phase currents. The work represented in this paper are mainly concentrated on classification of fault and this classification is done based on the obtained energy values after applying discrete wavelet transform by taking this values as an input for the neural network. The proposed system and analysis is carried out in Matlab Simulink.

Critical investigations on performance of ANN and wavelet fault classifiers

With increasing demands and competitive business environment, the structure of power system has become very complex. Moreover, power system is a dynamic framework due to faults and rapid load variations. Hence, the detection algorithms for faults are potential areas of research. To discuss this issue and to provide the solution methodology for detection of faults and further classification of those in a smart grid is a primary motivation of this manuscript. This paper presents application of supervised learning algorithms based on different neural network topologies for detection and classification of the faults in transmission lines in power system. Different wavelet transforms on different Multi Resolution Analysis levels are applied for detection of the potential features from the voltage waveforms of the Phasor Measurement Units (PMUs). These wavelet transforms are then applied to several neural networks classification engines to classify faults. Binary classification technique is used for definitions of faults. Different faults namely single line to ground, line to line, double line to ground and three phase symmetrical faults are designated as a binary digit. These definitions are employed to train the classification engine. Different plots of confusion and errors are plotted to establish a fair comparison between supervised learning algorithms.

Condition assessment strategies of composite insulator based on statistic methods

Composite insulator has significant advantages over porcelain and glass insulator for its light weight and anti-pollution flashover performance and has been widely used in overhead power transmission line systems. These advantages would become more remarkable at higher voltage level. In China, silicone composite insulator has been generally regarded as the first choice in UHV and UHVDC overhead transmission systems. However, as a polymer material, the aging threat of silicone rubber has always been a major concern in the application of composite insulators. It is not just important but also urgent to develop specific strategies to evaluate the operation condition of composite insulators and corresponding maintenance methods. In this paper, 65 composite insulator specimens have been collected and studied. A series of tests and measurements have been conducted and all test results have been gathered and filtered based on statistic methods to form the final condition assessment strategies. In this procedure, Fisher Discriminant analysis, Bayes Discriminant analysis and binary-logistic regression method have been applied to establish the relations between the operation condition of composite insulators and the measured data. A three-group and a two-group classification strategy has been developed in this paper to assess the operation condition of insulators.

The Line and Node Voltage Stability Index Presented Through the Porosity of High-voltage Transmission Lines

The porosity of power system high-voltage transmission lines is presented through the lens of the problem of voltage stability. The ability of transmission lines to receive and transport electric energy to load centers is determined based on the well-known relationships between the characteristic variables in the field of voltage stability. Using a simple procedure, it is possible to trace the effect of electrical power flows on power system transmission line porosity and detect unstable voltage states. Through the proposed approach, the planners and operators of transmission lines can continually calculate the effect of current power flows and transmission topology on the ability of transmission lines to receive more electric power without endangering the voltage stability and desired voltage profiles throughout a given system. Therefore, the results of the presented algorithm can operate in a manner to utilize the inherent available margins of the existing and planned transmission lines.

A survey on intelligent system application to fault diagnosis in electric power system transmission lines

Fault analysis and diagnosis constitute a relevant problem in power systems, with important economic impacts for operators, maintenance agents and the power industry in general. This has motivated the research and development of new algorithms and methods to address this problem. Intelligent systems have been proposed in the literature to deal with this problem in a significant number of applications. In the context of fault diagnosis in electric power systems, this survey presents a review of intelligent systems application to fault diagnosis in electric power system transmission lines. A huge number of related works can be found in the literature, being the major contributions reported in international journals. Then, the works cited in the present survey are restricted to those reported in regular journals that present high adherence to the aforementioned subject. The classification of strategies employed and their relationships with classical techniques are presented and discussed, allowing the identification of the main trends and research areas related to transmission line intelligent fault diagnosis systems.

Countermeasures on preventing backup protection mal-operation during load flow transferring

Abstract Removal of transmission lines in power systems may result in load flow transferring and unreasonable operations of backup protections, which may develop into cascading tripping accidents and even blackouts. Traditional backup protection based on local measurement information has disadvantages in preventing from cascading trip because it cannot distinguish the over load reason between due to faults and due to load flow transferring. On the premise that the load flow transferring can be identified based on wide-area coordination system, concise adaptive adjustment methods for the operation characteristics of zone III distance protection and overcurrent protections are proposed to avoid the mal-operation caused by load flow transferring. Meanwhile, their backup protection functionality can be reserve to a great extent. The simulation test results illustrate the validity of the proposed method.

Quickest Line Outage Detection and Identification

A method is proposed to detect and identify power system transmission line outages in near real-time. The method exploits the statistical properties of the small random fluctuations in electricity generation and demand that a power system is subject to as time evolves. To detect and identify transmission line outages, a linearized incremental small-signal power system model is used in conjunction with high-speed synchronized voltage phase angle measurements obtained from phasor measurement units. By monitoring the statistical properties of voltage phase angle time-series, line outages are detected and identified using techniques borrowed from the theory of quickest change detection. As illustrated through case studies, the proposed method is effective in detecting and identifying single- and double-line outages in an accurate and timely fashion.

Local Outlier Factor based Data Mining Model for Three Phase Transmission Lines Faults Identification

The main objective of this paper is to design and developed a model of power system transmission lines fault identification using Local Outlier Factor (LOF) technique based on data mining. 9 bus power system and 30 bus power systems overhead transmission line fault of are simulated as a test cases using PowerWorld software and the LOF computations using Matlab software. The proposed model consists of two main steps; the first step is used to determine single line-toground (SLG) fault and 3 phase fault. The second step was used to determine line-to-line (LL) fault and double line-toground (DLG) fault. The second step is required uninterrupted phase voltage in order to distinguish between LL fault and DLG faults. The proposed model successfully identified asymmetrical faults and symmetrical faults that particular single bus fault. General Terms Local Outlier Factor, Data Mining Model

RF control hardware design for CYCIAE-100 cyclotron

The Beijing Radioactive Ion-beam Facility project is being constructed by BRIF division of China Institute of Atomic Energy. In this project, a 100MeV high intensity compact proton cyclotron is built for multiple applications. The first successful beam extraction of CYCIAE-100 cyclotron was done in the middle of 2014. The extracted proton beam energy is 100MeV and the beam current is more than 20μA. The RF system of the CYCIAE-100 cyclotron includes two half-wavelength cavities, two 100kW tetrode amplifiers and power transmission line systems (all above are independent from each other) and two sets of Low Level RF control crates. Each set of LLRF control includes an amplitude control unit, a tuning control unit, a phase control unit, a local Digital Signal Process control unit and an Advanced RISC Machines based EPICS IOC unit. These two identical LLRF control crates share one common reference clock and take advantages of modern digital technologies (e.g. DSP and Direct Digital Synthesizer) to achieve closed loop voltage and phase regulations of the dee-voltage. In the beam commission, the measured dee-voltage stability of RF system is better than 0.1% and phase stability is better than 0.03°. The hardware design of the LLRF system will be reviewed in this paper.