Impacts of automatic control systems of loop restoration scheme on the distribution system reliability

Loop restoration scheme (LRS) is a special feeder automation (FA) scheme, which is used by utilities to improve distribution system reliability. The LRS is controlled and managed by its automatic control system (ACS). The impacts on distribution system reliability indices of implementing LRS mainly depend on the type of its ACS. Two common types of ACS of LRS are presented and used in this study. Successful operation of ACS is dependent on the protection and automatic control functions of switching devices of LRS. Different failure modes of these switching devices can therefore affect the procedure of ACS in fault detecting, isolating and service restoration. The impacts of failure of protection and automatic control functions of switching devices and fuse of lateral distributors on reliability indices are illustrated. The worth of implementing LRS and its ACS type is represented by the reduction in expected customer interruption cost. A distribution test system is utilised to examine the impacts of two common types of ACS of LRS on the distribution system reliability. Selecting the type of ACS of LRS by utilities relies on the desired level of load-point and system reliability improvement. This study aims to quantitatively assess the impacts of two common types of ACS of LRS on the distribution system reliability.     

Analysis of Ferroresonance Modes in Power Transformers Using Preisach-Type Hysteretic Magnetizing Inductance

The magnetic hysteresis phenomenon plays an important role in the ferroresonance behavior of a transformer. However, most existing transformer models for the analysis of the ferroresonance phenomenon either ignore the hysteresis effects or represent it based on the hysteresis major loop and scale it for lower current levels. Such a modeling approach does not reflect the actual physical behavior of the magnetic core with respect to the ferroresonance phenomenon. This paper, based on Preisach theory, introduces an accurate model of hysteretic inductor to represent a single-phase transformer for the investigation of the ferroresonance phenomenon. Based on the developed model, time-domain simulation studies show that even in the case of a single-phase transformer where no magnetic coupling exists between the three-phases, all known types of ferroresonance, including fundamental, sub-harmonic, quasi-periodic and chaotic modes, may occur. The effects of hysteresis and its initial conditions, grading capacitors and transformer winding capacitance on occurrence of ferroresonance and its change of mode are also discussed     

High-impedance fault detection using multi-resolution signal decomposition and adaptive neural fuzzy inference system

High-impedance faults (HIFs) on distribution systems create unique challenges to protection engineers. HIFs do not produce enough fault current to be detected by conventional overcurrent relays or fuses. A method for HIF detection based on the nonlinear behaviour of current waveforms is presented. Using this method, HIFs can be distinguished successfully from other similar waveforms such as nonlinear load currents, secondary current of saturated current transformers and inrush currents. A wavelet multi-resolution signal decomposition method is used for feature extraction. Extracted features are fed to an adaptive neural fuzzy inference system (ANFIS) for identification and classification. The effect of choice of mother wavelet is also analysed by investigating a large number of wavelet families. Various simulation results, which are obtained using an appropriate model, are summarised and efficiency of the proposed algorithm for dependable and secure HIF detection is determined.     

Prediction of leakage current of non-ceramic insulators in early aging period

The paper presents a neural network based prediction technique for the leakage current (LC) of non-ceramic insulators during salt-fog test. Nearly 50 distribution class silicone rubber (SIR) insulators with three different voltage classes have been tested in a salt-fog chamber, where the LC has been continuously recorded for at least 100 h. A boundary for early aging period is defined by the rate of change of the LC instead of a fixed threshold value. Consequently, the Gaussian radial basis network has been adopted to predict the level of LC at the early stage of aging of the SIR insulators and is compared with a classical network. The initial values of LC and its rate of change at 10 min intervals for the first 5 h are selected as the input to the network, and the final value of LC of the early aging period is considered as the output of the network. It is found that Gaussian radial basis function network with a random optimizing training method is an appropriate network to predict the LC with a 3.5–5.3% accuracy, if the training data and the testing data are selected from the same type of SIR insulators.     

Correction of saturated current transformers secondary current using ANNs

Current transformers (CTs) provide instrument-level current signals to meters and protective relays. Protective relays' accuracy and performance are directly related to steady-state and transient performance of CTs. CT saturation could lead to protective relay maloperation or even prevent tripping. This paper proposes the use of an artificial neural networks scheme to correct CT secondary waveform distortions. The proposed module uses samples of current signals to achieve the inverse transfer function of CT. Simulation studies are preformed and the influence of changing different parameters is studied. Performance studies results show that the proposed algorithm is accurate and reliable. The proposed algorithm has also been implemented and tested on a digital signal processor board. Details of the implementation and experimental studies are provided in this paper.     

Implementation and laboratory test results of an Elmannetwork-based transmission line directional relay

A transmission line fault direction identification module based on an Elman recurrent network has been implemented on a DSP board and its behavior is investigated on a physical power system model. Details of implementation and the experimental studies are given and analysed in the paper. Studies show that the proposed approach is able to identify the direction of a fault on a transmission line rapidly and correctly. It is suitable for realizing a fast transmission line directional comparison protection scheme     

High speed transmission system directional protection using anElman network

Detection of the direction of a fault on a transmission line is essential to the proper performance of a power system. It would be desirable to develop a high speed and accurate approach to determine the fault direction for different power system conditions. To classify forward and backward faults on a given line, a neural network's abilities in pattern recognition and classification could be considered as a solution. To demonstrate the applicability of this solution, neural network technique is employed and a novel Elman recurrent network is designed and trained. Details of the design procedure and the results of performance studies with the proposed network are given and analysed in the paper. System simulation studies show that the proposed approach is able to detect the direction of a fault on a transmission line rapidly and correctly. It is suitable to realize a very fast transmission line directional comparison protection scheme