Passive Islanding Detection Technique for Integrated Distributed Generation at Zero Power Balanced Islanding

Renewable power generation systems have more advantages in the integrated power system compared to the generation due to fossil fuels because of their advantages like reliability and power quality. One of the important problems due to such renewable distributed generation (DG) system is an unintentional islanding. Islanding is caused if DG supplies power to load after disconnecting from the grid. As per the DG interconnection standards, it is required to detect the islanding within two seconds after islanding with the equipments connected to it. In this paper a new passive islanding detection method is presented for wind DG integrated power system with rate of change of positive sequence voltage (ROCOPSV) and rate of change of positive sequence current (ROCOPSC). The islanding is detected if both the values of ROCOPSV and ROCONSV are more than a predefined threshold value. The test system results carried on MATLAB shows the performance of the proposed method for various islanding and non islanding events with different power imbalances. The results conclude that, this method can detect islanding even at balanced islanding with zero non detection zone (NDZ)

Real-time power system dynamic simulation

The present day digital computing resources are overburdened by the amount of calculation necessary for power system dynamic simulation. Although the hardware has improved significantly, the expansion of the interconnected systems, and the requirement for more detailed models with frequent solutions have increased the need for simulating these systems in real time. To achieve this, more effort has been devoted to developing and improving the application of numerical methods and computational techniques such as sparsity-directed approaches and network decomposition to power system dynamic studies. This project is a modest contribution towards solving this problem. It consists of applying a very efficient sparsity technique to the power system dynamic simulator under a wide range of events. The method used was first developed by Zollenkopf (^117) Following the structure of the linear equations related to power system dynamic simulator models, the original algorithm which was conceived for scalar calculation has been modified to use sets of 2 * 2 sub-matrices for both the dynamic and algebraic equations. The realisation of real-time simulators also requires the simplification of the power system models and the adoption of a few assumptions such as neglecting short time constants. Most of the network components are simulated. The generating units include synchronous generators and their local controllers, and the simulated network is composed of transmission lines and transformers with tap-changing and phase-shifting, non-linear static loads, shunt compensators and simplified protection. The simulator is capable of handling some of the severe events which occur in power systems such as islanding, island re-synchronisation and generator start-up and shut-down. To avoid the stiffness problem and ensure the numerical stability of the system at long time steps at a reasonable accuracy, the implicit trapezoidal rule is used for discretising the dynamic equations. The algebraisation of differential equations requires an iterative process. Also the non-linear network models are generally better solved by the Newton-Raphson iterative method which has an efficient quadratic rate of convergence. This has favoured the adoption of the simultaneous technique over the classical partitioned method. In this case the algebraised differential equations and the non-linear static equations are solved as one set of algebraic equations. Another way of speeding-up centralised simulators is the adoption of distributed techniques. In this case the simulated networks are subdivided into areas which are computed by a multi-task machine (Perkin Elmer PE3230). A coordinating subprogram is necessary to synchronise and control the computation of the different areas, and perform the overall solution of the system. In addition to this decomposed algorithm the developed technique is also implemented in the parallel simulator running on the Array Processor FPS 5205 attached to a Perkin Elmer PE 3230 minicomputer, and a centralised version run on the host computer. Testing these simulators on three networks under a range of events would allow for the assessment of the algorithm and the selection of the best candidate hardware structure to be used as a dedicated machine to support the dynamic simulator. The results obtained from this dynamic simulator are very impressive. Great speed-up is realised, stable solutions under very severe events are obtained showing the robustness of the system, and accurate long-term results are obtained. Therefore, the present simulator provides a realistic test bed to the Energy Management System. It can also be used for other purposes such as operator training

Machine Learning Approach to Islanding Detection for Inverter-Based Distributed Generation

Despite a number of economic and environmental benefits that integration of renewable distributed generation (DG) into the distribution grid brings, there are many technical challenges that arise as well. One of the most important issues concerning DG integration is unintentional islanding. Islanding occurs when DG continues to energize portion of the system while being disconnected from the main grid. Since the island is unregulated, its behavior is unpredictable and voltage, frequency and other power system parameters may have unacceptable levels, which may cause hazardous effect on devices and public. According to the IEEE Standard 1547 DG shall detect any possible islanding conditions and cease to energize the area within 2 sec. In this dissertation work, a new islanding detection method for single phase inverter-based distributed generation is presented. In the first stage of the proposed method, parametric technique called Autoregressive (AR) signal modeling is utilized to extract signal features from voltage and current signals at the Point of Common Coupling (PCC) with the grid. In the second stage, advanced machine learning technique based on Support Vector Machine (SVM) which takes calculated features as inputs is utilized to predict islanding state. The extensive study is performed on the IEEE 13 bus system and feature vectors corresponding to various islanding and non-islanding conditions, such as external grid faults and power system components switching, are used for SVM classifier training and testing. Simulation results show that proposed method is robust to external grid transients and able to accurately discriminate islanding conditions 50ms after the event begins

L'intertextualité dans les publications scientifiques

La base de données bibliographiques de l'IEEE contient un certain nombre de duplications avérées avec indication des originaux copiés. Ce corpus est utilisé pour tester une méthode d'attribution d'auteur. La combinaison de la distance intertextuelle avec la fenêtre glissante et diverses techniques de classification permet d'identifier ces duplications avec un risque d'erreur très faible. Cette expérience montre également que plusieurs facteurs brouillent l'identité de l'auteur scientifique, notamment des collectifs de chercheurs à géométrie variable et une forte dose d'intertextualité acceptée voire recherchée

KOORDINASI PROTEKSI ADAPTIF RELE ARUS LEBIH DIGITAL MENGGUNAKAN METODA ARTIFICIAL NEURAL NETWORK PADA SISTEM MESH DENGAN PEMBANGKIT TERSEBAR

Pada sistem mesh yang terkoneksi dengan pembangkit tersebar (Distributed Generation), terdapat kondisi dimana topologi jaringan yang berubah-ubah. Hal tersebut disebabkan oleh waktu operasi dari pembangkit tersebar yang bersifat temporary dan random. Kondisi ini dapat dibedakan yaitu terhubung dengan grid, terhubung grid dan DG1, terhubung grid dan DG2, dan terhubung grid dengan DG1 dan DG2. Akibat topologi jaringan yang berubah-ubah, menyebabkan peningkatan dan penurunan level arus hubung singkat sehingga seting dan koordinasi proteksi awal menjadi tidak efektif dan efisien lagi terhadap konfigurasi jaringan yang ada. Oleh karena itu dibutuhkan sistem proteksi yang setingnya dapat menyesuaikan dengan topologi jaringan yang berubah-ubah. Pada tugas akhir ini akan dirancang koordinasi sistem proteksi rele arah arus lebih yang dapat mengikuti setiap perubahan kondisi pada topologi jaringan tersebut menggunakan metoda Artificial Neural Network dengan desain plant berbentuk mesh yang terhubung dengan Grid dan DG. Hasil perancangan menunjukan bahwa penggunaan metoda Artificial Neural Network dapat menghasilkan setingan rele yang adaptif mengikuti perubahan topologi sistem. Dan metoda artificial neural network juga dapat memprediksi setingan rele pada saat terjadi gangguan yang diluar kondisi yang telah dipelajari dalam data learning

Optimal placement of phasor measurement units using the Advanced Matrix Manipulation algorithm

Includes abstract.Includes bibliographical references.This thesis investigates the problem of the Optimal Placement scheme of Phasor Measurement Units in electrical power systems for State Estimation to facilitate improved monitoring and control of the system parameters. The research work done for this thesis begins with review of Supervisory Control and Data Acquisition systems (SCADA). SCADA-based systems are currently employed for condition monitoring and control of industrial and utility electrical power systems. For utility power networks, the main problem with voltage and current phasor data captured by SCADA systems is that they are not synchronised with respect to each other in a present-time or Real-time framework. This implies that both magnitude and phase angle of the measured phasors tend to get affected by slow data flow provided by SCADA to the points of utilization and also by differences in time instants of data capture. These factors inhibit theefficiency and quality of the power system monitoring and control. “Phasor Measurement Unit” (PMU) is a relatively new technology that, when employed in power networks, offers real-time synchronised measurements of the voltages at buses and currents along the lines that connect them. This is accomplished by using a GPS based monitoring system which facilitates time synchronisation of measurements and unlike SCADA, makes the measured data available in Real-Time format. SCADA is not able to provide Real-time data due to the low speeds at which RTUs (Remote Terminal Units) provide data. Availability of time-stamped phasor measurements makes PMUs preferable for power system monitoring and control applications such as State Estimation, Instability Prediction Analysis, Real-time Monitoring of the system conditions, Islanding Detection, System Restoration and Bad Data Detection