Manoj Tripathy

Power Transformer Differential Protection Based On Optimal Probabilistic Neural Network

In this paper, the optimal probabilistic neural network (PNN) is proposed as the core classifier to discriminate between the magnetizing inrush and the internal fault of a power transformer. The particle swarm optimization is used to obtain an optimal smoothing factor of PNN which is a crucial parameter for PNN. An algorithm has been developed around the theme of the conventional differential protection of the transformer. It makes use of the ratio of voltage-to-frequency and amplitude of differential current for the determination of operating condition of the transformer. The performance of the proposed heteroscedastic-type PNN is investigated with the conventional homoscedastic-type PNN, feedforward back propagation (FFBP) neural network, and the conventional harmonic restraint method. To evaluate the developed algorithm, relaying signals for various operating condition of the transformer, including internal and external faults, are obtained by modeling the transformer in PSCAD/EMTDC. The protection algorithm is implemented by using MATLAB.

Advances in Transformer Protection: A Review

In this article, an attempt is made to put together developments in digital relays for protection of power transformer. Efforts have been made to include all the techniques and philosophies used to that end. The article includes the most recent techniques, like artificial neural network (ANN) and fuzzy logic concepts, as well as other conventional methods used for transformer protection. Only a brief mention is made about conventional methods, while more emphasis is given on the application of ANN and fuzzy techniques for the protection of power transformer. This article presents a set of references of all concerned papers and provides a brief summary of the work presented in them. It also presents the results of these techniques as provided in the respective references.

An Innovative Pilot Relaying Scheme for Shunt-Compensated Line

This paper presents a fault detection and classification scheme for a shunt [static var compensator (SVC)] compensated line. The proposed relaying scheme is based on the concept of superimposed sequence components-based integrated impedance (SSCII). For an internal fault, the magnitude of SSCII is small and for an external fault, it is very large. In the SVC compensated line, a fault forces SVC to vary its impedance. Superimposed components are injected due to this impedance variation, along with the fault components. These SVC-injected components are treated as fault-injected components and, therefore, even the sound phases are detected as faulty phases. To avoid such failure, fault-injected superimposed components have been extracted by using the modified prefault data, which is estimated according to SVC impedance variations. The superimposed components measured using the modified prefault data consist of fault-injected superimposed components only. The proposed scheme has been tested for all types of faults, different values of fault resistances, and several fault locations and SVC locations. The results demonstrate that the proposed scheme successfully detects and classifies the faults. Also, the proposed scheme is robust against variations in fault resistance, source impedance, and SVC location.

A radial basis function neural network approach for multi-hour short term load-price forecasting with type of day parameter

In 1990s, after deregulation of Australian electricity market, electricity became a commodity that can be bought and sold. This led power industry to change their planning strategies. In this planning Short Term Load Forecasting (STLF) plays a vital role to provide unit commitment, economic generation scheduling etc. In this paper, RBF neural network (RBFNN) is applied as short term load as well as price forecaster. While modeling process, day-type (Sunday, Monday, etc.) is considered as an extra input to the neural network. The prediction performance of proposed RBFNN architecture is evaluated using Mean of Mean Absolute Percentage Error (MMAPE) between actual data and forecasted data of New South Wales (Australia). The results obtained are compared with the results gained from classical moving average (MA), Holt-Winters and Feed Forward Neural Network (FFNN) methods. It is, in general, observed that the RBFNN is more accurate and works better with inclusion of day type input parameters.

Neuro-fuzzy Technique for Power Transformer Protection

Abstract In the quest of more reliable power transformer protection, differential protection is considered the best scheme. In the proposed scheme, various operating conditions of transformers are distinguished by virtue of the signatures of differential current. As the conditions of internal fault and magnetizing inrush do have some of the signatures common among them, it is becoming increasingly important and difficult to distinguish between magnetizing inrush and fault conditions for differential relaying. In this direction, both feature-based, as well as pattern-based, approaches are used. In this article, a new approach, based on neuro-fuzzy technique, is presented for power transformer protection that ensures relay stability against external fault, magnetizing inrush, sympathetic inrush, and over-excitation conditions and its operation on internal faults. This approach is able to handle the “vague” information rather than only the “crisp” information. In the proposed method, fuzzy back-propagation neural network (FBPNN) is used as a core classifier to discriminate between magnetizing inrush and internal fault of a power transformer. An algorithm has been developed using an optimal number of neurons in the hidden layer as well as in the output layer. The effect of hidden layer neurons on the classification accuracy is analyzed. The algorithm makes use of voltage-to-frequency ratio and amplitude of differential current for detection of transformer operating conditions. The performance of BPNN, radial basis function neural network (RBFNN), and probabilistic neural network (PNN) are compared with the proposed fuzzy BPNN. Extensive simulation studies have been performed to demonstrate the efficiency of the proposed scheme using PSCAD/EMTDC and MATLAB.

Advance Compensated Mho Relay Algorithm for a Transmission System with Shunt Flexible AC Transmission System Device

Abstract—The presence of shunt flexible AC transmission system devices adversely affect the performance of distance relay and create security and reliability issues. This article introduces a noble compensated Mho relay algorithm for the protection of transmission line employing shunt flexible AC transmission system devices, such as a static VAR compensator and static synchronous compensator. A detailed model of transmission system employing a shunt flexible AC transmission system device is explained. Then compensated impedance inserted by a shunt device in the transmission line is calculated, and finally, a compensated Mho relay algorithm is proposed to protect zone one of the transmission line. Simulation work is carried out in PSCAD/EMTP software. Results show that the proposed relay is secure, accurate, and reliable under the wide variation in power system parameters, such as load angle, fault resistance, fault location, and compensation level.

Neural Network Principal Component Analysis based power transformer differential protection

This paper describes a new approach for power transformer protection that ensures the security for external faults, magnetizing inrush and over-excitation conditions and provides dependability for internal faults. This approach based on the wave-shape recognition technique. An algorithm based on Neural Network Principal Component Analysis (NNPCA) with back propagation learning is proposed for digital differential protection of power transformer. The principal component analysis is used to preprocess the data from power system in order to eliminate redundant information and enhance hidden pattern of differential current to discriminate between internal faults from inrush and over-excitation conditions. This algorithm has been developed by considering optimal number of neurons in hidden layer and optimal number of neurons at output layer. The effect of hidden layer neurons on the classification accuracy is analyzed. The proposed algorithm makes use of ratio of voltage-to-frequency and amplitude of differential current for transformer operating condition detection. The algorithm is evaluated using simulation performed with PSCAD/EMTDC and MATLAB.

Wavelet transform based discrimination between inrush and internal fault of indirect symmetrical phase shift transformer

The non-sinusoidal inrush current has high magnitude and hence the discrimination from the other operating conditions such as internal faults becomes difficult in the protection of a power transformer. This paper proposes an effective method based on wavelet transform for the differentiation between inrush current and internal fault current in indirect symmetrical phase shift transformer (ISPST). Conventional Parsevals theorem has been used to calculate the wavelet energy of the differential current and a suitable threshold is decided for the discrimination between inrush and internal fault condition of ISPST. Different types of internal fault and inrush current conditions under a wide range of switching angle have been considered for the verification of the proposed method in the present simulation study. PSCAD/EMTDC has been utilized as simulation plateform.

An integrated impedance based pilot protection scheme for SVC compensated transmission line

This paper presents a fault detection and classification scheme, based on integrated impedance, for SVC compensated transmission line. For internal faults, integrated impedance of faulty phase is low and for healthy phase, it is high. For all external faults, integrated impedances of all phases are high. The protection scheme based on this concept is used for variable impedance shunt compensation (i.e. Static Var Compensator, SVC). Proposed scheme has been analyzed for each phase with different fault resistances, fault types and fault locations. The results demonstrate the effectiveness of the proposed scheme.

Power Transformer Differential Protection Based on Neural Network Principal Component Analysis, Harmonic Restraint and Park's Plots

This paper describes a new approach for power transformer differential protection which is based on the wave-shape recognition technique. An algorithm based on neural network principal component analysis (NNPCA) with back-propagation learning is proposed for digital differential protection of power transformer. The principal component analysis is used to preprocess the data from power system in order to eliminate redundant information and enhance hidden pattern of differential current to discriminate between internal faults from inrush and overexcitation conditions. This algorithm has been developed by considering optimal number of neurons in hidden layer and optimal number of neurons at output layer. The proposed algorithm makes use of ratio of voltage to frequency and amplitude of differential current for transformer operating condition detection. This paper presents a comparative study of power transformer differential protection algorithms based on harmonic restraint method, NNPCA, feed forward back propagation neural network (FFBPNN), space vector analysis of the differential signal, and their time characteristic shapes in Park’s plane. The algorithms are compared as to their speed of response, computational burden, and the capability to distinguish between a magnetizing inrush and power transformer internal fault. The mathematical basis for each algorithm is briefly described. All the algorithms are evaluated using simulation performed with PSCAD/EMTDC and MATLAB.