Using Support Vector Machine for Prediction Dynamic Voltage Collapse in an Actual Power System

Abstract—This paper presents dynamic voltage collapse prediction on an actual power system using support vector machines. Dynamic voltage collapse prediction is first determined based on the PTSI calculated from information in dynamic simulation output. Simulations were carried out on a practical 87 bus test system by considering load increase as the contingency. The data collected from the time domain simulation is then used as input to the SVM in which support vector regression is used as a predictor to determine the dynamic voltage collapse indices of the power system. To reduce training time and improve accuracy of the SVM, the Kernel function type and Kernel parameter are considered. To verify the effectiveness of the proposed SVM method, its performance is compared with the multi layer perceptron neural network (MLPNN). Studies show that the SVM gives faster and more accurate results for dynamic voltage collapse prediction compared with the MLPNN. Keywor ds —Dynamic voltage collapse, prediction, artificial neural network, support vector machines

Performance Evaluation of Fuel Cell and Microturbine as Distributed Generators in a Microgrid

This paper presents dynamic models of distributed generators (DG) and investigates dynamic behaviour of the DG units within a microgrid system. The DG units include micro turbine, fuel cell and the electronically interfaced sources. The voltage source converter is adopted as the electronic interface which is equipped with its controller to maintain stability of the microgrid during small signal dynamics. This paper also introduces power management strategies and implements the DG load sharing concept to maintain the microgrid operation in standalone, grid-connected and islanding modes of operation. The results demonstrate the operation and performance of the microturbine and SOFC as distributed generators in a microgrid. Keywords: Microgrid, Distributed Generation, Microturbine, Fuel Cel

Support Vector Regression Based S-transform for Prediction of Single and Multiple Power Quality Disturbances

This paper presents a novel approach using Support Vector Regression (SVR) based S-transform to predict the classes of single and multiple power quality disturbances in a three-phase industrial power system. Most of the power quality disturbances recorded in an industrial power system are non-stationary and comprise of multiple power quality disturbances that coexist together for only a short duration in time due to the contribution of the network impedances and types of customers’ connected loads. The ability to detect and predict all the types of power quality disturbances encrypted in a voltage signal is vital in the analyses on the causes of the power quality disturbances and in the identification of incipient fault in the networks. In this paper, the performances of two types of SVR based S-transform, the non-linear radial basis function (RBF) SVR based S-transform and the multilayer perceptron (MLP) SVR based S-transform, were compared for their abilities in making prediction for the classes of single and multiple power quality disturbances. The results for the analyses of 651 numbers of single and multiple voltage disturbances gave prediction accuracies of 86.1% (MLP SVR) and 93.9% (RBF SVR) respectively. Keywords: Power Quality, Power Quality Prediction, S-transform, SVM, SV

Vibration based piezoelectric energy harvesting utilizing bridgeless rectifier circuit

The energy harvesting technique has the capability to build autonomous, self-powered electronic systems that does not depend on the battery power for driving the low power electronics devices. In this paper, a voltage doubler and bridgeless boost rectifier power electronic converter is proposed to increase the energy harvesting output voltage from piezoelectric vibration based transducer. The conventional full-wave diode bridge rectifier and boost converter for energy harvesting system increases significant voltage drop and power losses in the circuit. However, the proposed voltage doubler and bridgeless boost rectifier circuit reduce the voltage drop and power loses in the circuit and thus increases the efficiency of the circuit. The proposed voltage doubler and bridgeless boost rectifier circuit step-up the output voltage up to 3 V DC from an input voltage of 1.9 V AC

Performance Evaluation of Voltage Stability Indices for Dynamic Voltage Collapse Prediction

The research presents a study in evaluating the performance of several voltage stability index has been proposed and it is named as the power transfer stability index. The proposed index is then compared with other known voltage stability indices such as the voltage collapse prediction index, the line index and power margin. To evaluate and compare the effectiveness of these indices in predicting proximity to voltage collapse, simulation are carried out using the WSCC 9 bus test system. Simulation test result show that the proposed power transfer ability index and the voltage collapse prediction index give a better prediction of dynamic voltage collapse comapared to the power margin and the line index

Transient stability emergency control of power systems employing UFLS combined with generator tripping method

This paper concerns with transient stability control which is part of transient stability assessment which needs to be considered so that the power systems remained intact when failures originating from faults occurred in power systems. Conventional UFLS system is designed to retrieve the balance of generation and consumption following disturbances occurrences in the system. In UFLS method, whenever the system's frequency drops below a predetermined value, the system loads are shed in stages. An efficient UFLS method needs to be devised so as to reduce the impacts of transient disturbance on power systems and prevent total system blackout. In this paper, an emergency control scheme known as the combined UFLS and generator tripping is developed in order to stabilize the system when unstable faults occurred in a power system. The performance of the combined UFLS and generator tripping scheme is compared with the conventional UFLS control scheme. The results show that the combined control scheme performed better

Area-based COI-referred transient stability index for large-scale power system

This paper presents a new transient stability index called the Area-based COI-referred Transient Stability Index for a large electrical power system. A large power system is divided into smaller areas depending on the coherency of the system due disturbances before the index is applied on the system. The proposed index is defined by associating with each area of the power system an equivalent inertia representing the total inertia of the generation located in that area. Assuming that each area is coherent, it is possible to assimilate its behavior to that of a single large machine with same inertia and generation. It also offers a direct means of deriving the centre of inertia (COI). The COI provides very useful information for tracking the stability of interconnected areas. So, instead of assessing all generators’ rotor angles. Simulations on the large practical power system show the effectiveness of the proposed index

Transient stability emergency control using generator tripping based on tracking area-based rotor angle combined with UFLS.

This paper concerns with transient stability control which is part of transient stability assessment which needs to be considered so that the power systems remained intact when failures originating from faults occurred in power systems. Conventional under frequency load shedding (UFLS) system is designed to retrieve the balance of generation and consumption following disturbances occurrences in the system. In UFLS method, whenever the system's frequency drops below a predetermined value, the system loads are shed in stages. An efficient UFLS method needs to be devised so as to reduce the impacts of transient disturbance on power systems and prevent total system blackout. In this paper, an emergency control scheme known as the combined UFLS and generator tripping is developed in order to stabilize the system when unstable faults occurred in a power system. The performance of the combined UFLS and generator tripping scheme is compared with the conventional UFLS control scheme. The results show that the combined control scheme performed better

Performance Comparison of Artificial Intelligence Techniques for Non-intrusive Electrical Load Monitoring

The increased awareness in reducing energy consumption and encouraging response from the use of smart meters have triggered the idea of non-intrusive load monitoring (NILM). The purpose of NILM is to obtain useful information about the usage of electrical appliances usually measured at the main entrance of electricity to obtain aggregate power signal by using a smart meter. The load operating states based on the on/off loads can be detected by analysing the aggregate power signals. This paper presents a comparative study for evaluating the performance of artificial intelligence techniques in classifying the type and operating states of three load types that are usually available in commercial buildings, such as fluorescent light, air-conditioner and personal computer. In this NILM study, experiments were carried out to collect information of the load usage pattern by using a commercial smart meter. From the power parameters captured by the smart meter, effective signal analysis has been done using the time time (TT)-transform to achieve accurate load disaggregation. Load feature selection is also considered by using three power parameters which are real power, reactive power and the TT-transform parameters. These three parameters are used as inputs for training the artificial intelligence techniques in classifying the type and operating states of the loads. The load classification results showed that the proposed extreme learning machine (ELM) technique has successfully achieved high accuracy and fast learning compared with artificial neural network and support vector machine. Based on validation results, ELM achieved the highest load classification with 100% accuracy for data sampled at 1 minute time interval

Comparative Study in Determining Features Extraction for Islanding Detection using Data Mining Technique: Correlation and Coefficient Analysis

A comprehensive comparison study on the datamining based approaches for detecting islanding events in a power distribution system with inverter-based distributed generations is presented. The important features for each phase in the island detection scheme are investigated in detail. These features are extracted from the time-varying measurements of voltage, frequency and total harmonic distortion (THD) of current and voltage at the point of common coupling. Numerical studies were conducted on the IEEE 34-bus system considering various scenarios of islanding and non-islanding conditions. The features obtained are then used to train several data mining techniques such as decision tree, support vector machine, neural network, bagging and random forest (RF). The simulation results showed that the important feature parameters can be evaluated based on the correlation between the extracted features. From the results, the four important features that give accurate islanding detection are the fundamental voltage THD, fundamental current THD, rate of change of voltage magnitude and voltage deviation. Comparison studies demonstrated the effectiveness of the RF method in achieving high accuracy for islanding detection