Papia Ray

Artificial Intelligence Based Fault Location in a Distribution System

A hybrid technique for fault distance estimation in a distribution line with wind farm is presented in this paper. Here, one cycle of post fault current samples are taken for fault location from the distributed generation end. The collected samples are then decomposed by wavelet transform and thereafter six statistical features are extracted from the reconstructed detail coefficients of the current signal. Further best features are selected from the total feature set by forward feature selection method. These selected features are then fed as input to the artificial neural network for fault location. In the proposed method, the simulation conditions for the test pattern are completely different from the train one in order to make it robust. Simulation result shows that the proposed hybrid fault location method gives high accuracy for the distribution system.

Extreme learning machine based fault classification in a series compensated transmission line

This paper presents an accurate hybrid method for fault classification in a series compensated transmission line. The proposed scheme uses discrete wavelet transform in combination with extreme learning machine for fault classification. Instantaneous value of current signal is measured from the relaying end of the transmission line for one cycle duration from the inception of fault. Discrete wavelet transform is used to decompose the signal and extract certain features from it. The feature set is then normalized and best features are selected from the total feature set by forward feature selection method. Selected features are then fed as an input to the extreme learning machine for fault classification. To evaluate the feasibility of the proposed technique, it is tested on a 400 kV, 300 km series compensated transmission line for all the ten type of fault using MATLAB/ SIMULINK. A wide range of simulation condition is taken to generate the train and test pattern. Simulation result indicates that the proposed approach is robust, fast in learning and classifies the fault very accurately.

Signal processing technique based fault location of a distribution line

Hybrid signal processing technique is discussed in this paper to sense the fault in a 11 kV, 30 km distribution line with R-L load placed at the receiving end. The proposed method uses 1 cycle post fault voltage and current signal wave form sending end of the system under study. Further preprocessing of the collected signal is done by wavelet packet transform and discrete wavelet transform which includes decomposition of the signal and feature extraction. Thereafter from the total feature set, redundant features are removed and best features are selected by genetic algorithm based feature selection method to get better accuracy and reduce computational difficulty. To simulate the model accurately, sampling frequency taken is 30 kHz. Train and test data set are generated by considering various operating conditions which are made entirely different in order to make the suggested method insensitive to parameter variations. Artificial neural network /Support vector machine is used for prediction purpose. Then optimal features are fed to artificial neural network/ support vector machine to detect the fault location. It was observed from the simulation outcomes that the suggested method is quite accurate and fast as compared to schemes investigated by other researchers.

Fast and accurate fault location by extreme learning machine in a series compensated transmission line

This paper presents an improved hybrid approach for fault location in a series compensated transmission line. The proposed method uses one cycle post fault current and voltage samples. Thereafter features of faulty signal are extracted by wavelet transform. Best features are then selected by genetic algorithm based feature selection method and are fed as input to the extreme learning machine for fault location. The performance of the proposed method has been evaluated on a 300 km, 400 kV transmission line with thyristor controlled series capacitor placed at the middle. The proposed scheme has been tested for a wide variety of operating conditions like different fault inception angle, fault resistance, fault location and type of fault. Simulation result shows that the proposed method is quite fast and accurate for fault location in a series compensated transmission line.

A computational intelligence approach for fault location in transmission lines

The main objective of this paper is to accurately estimate the fault location in a transmission line. Accurate estimation of transmission line fault location will lead to quicker restoration of the supply. At the relay location, the instantaneous values of faulty current, voltage and power signals are available. The available signals are decomposed using 13-level Discrete Wavelet Transform (DWT). From the decomposed signals, the statistical features are obtained. Using forward feature selection algorithm, the best feature set is selected. These features are then applied to an artificial feed forward neural network (FNN) for estimating the fault distance. The proposed fault locator has been trained for different fault scenarios (fault resistance and phase difference) and tested with both integer and non-integer distance values. The test results demonstrate that the adopted technique is a reliable method for estimating fault locations accurately on transmission lines.

Hybrid methodology for short-term load forecasting

The main objective of this paper is to accurately forecast the short-term loads using Discrete Wavelet Transform (DWT) in combination with Artificial Neural Network/ Support Vector Machine. The complete analysis has been carried out using Temperature, Humidity, Dew Point and Actual loads as features. Here, 8-level DWT decomposition has been done to extract the 8 detailed and approximation coefficients, which are also used as features. Thereafter to enhance the accuracy, four optimal features are selected from the total feature set using Forward Feature Selection Algorithm during the training process during ANN/ SVM. The test data with the optimal features were then fed to the ANN or SVM for load forecasting. Here MAPE has been considered as the performance index. The test results demonstrate that the proposed technique is quite accurate to forecast the loads.

Short term load forecasting by artificial neural network

Electrical power load forecasting has at all times been an major issue in energy trade. Load forecasting is generally made through developing models on relative knowledge, reminiscent of local weather and previous load demand knowledge. Such forecast is almost always aimed towards brief-time period prediction like one-day forward prediction, on the ground that longer interval prediction (mid-term or long term) will not be reliant as a result of error propagation. The accurateness of load predicting needs a massive effect on an electricity services process and making cost. Exact load predicting is hence significant, particularly with the fluctuation shappening within the utility industry because of deregulation and competition. Several outmoded approaches such as regression model, time series model and expert system have been proposed for short term load forecasting by different degree of achievement. Artificial Neural Network established short term load forecasting model has its own importance due to its transparent model, easy implementation, and superior performance. In this paper ANN trained through back propagation in combination with Genetic Algorithm model is used aimed at short term load forecasting. In back propagation, the weights of neuron changes according to the gradient descent which may tend to local minima, so Genetic Algorithm is implemented which gives better forecasting result as compared to back propagation.

Hybrid technique for fault location of a distribution line

This paper presents a hybrid technique for fault location in a 11 KV, 30 km distribution line with the R-L load placed at the receiving end. The method proposed in this paper analyzes with post-fault sending end one cycle current signal of the distribution system. Preprocessing of the raw signal is done by wavelet packet transform to acquire the information of frequency sub-bands. Here, four level decomposition is performed by wavelet packet transform having sampled frequency of 30 kHz. Thereafter energy feature is collected from the decomposed coefficient for further preprocessing. From a total set of 16 features, 6 optimal features are selected by a feature selection method during the training process. Train and test matrix are produced by applying various simulation conditions like the fault inception angle, resistance of faults path, location of the fault and fault type. The operating conditions of train data set are made entirely dissimilar from the test data set in order to make the method robust to parameter variations. SVM (Support vector machine) and RBFNN (radial basis function neural network) is used for fault distance prediction. Thereafter the optimal features with the test data set are fed to the SVM (support vector machine) and RBFNN (radial basis function neural network) for fault distance estimation. It was seen from the results that wavelet packet transform with particle swarm optimization based feature selection provides minimum fault location error less than 0.21% as compared to other schemes discussed by various researchers.

Short-Term Load Forecasting Using Genetic Algorithm

Electrical power load forecasting has at all conditions been a basic subject in the energy trade. Load forecasting requires relative learning, reminiscent of neighborhood climate, and past load request information. The precision of load anticipating needs a huge impact on a power organization’s system and making cost. Review load forecasting is along these lines essential, especially with the progressions happening inside the utility business in light of deregulation and dispute. A few outmoded approaches, for example, regression model, time approach model and pro framework have been proposed for without a moment’s hesitation stack deciding by various levels of accomplishment. In this paper, ANN arranged through back development in the mix with the genetic algorithm is utilized. In back spread, the weights of neuron change as indicated by the edge plunge which may look out for close-by minima, so genetic algorithm is executed with backpropagation.

Fault detection, location and classification of a transmission line

Abstract Transient stability is very important in power system. Large disturbances like fault in a transmission line are a concern which needs to be disconnected as quickly as possible in order to restore the transient stability. Faulty current and voltage signals are used for location, detection and classification of faults in a transmission network. Relay detects an abnormal signal, and then the circuit breaker disconnects the unhealthy transmission line from the rest of the health system. This paper discusses various signal processing techniques, impedance-based measurement method, travelling wave phenomenon-based method, artificial intelligence-based method and some special technique for the detection, location and classification of various faults in a transmission network. In this survey, paper signifies all method and techniques till August 2017. This compact and effective survey helps the researcher to understand different techniques and methods.