It is important to find an effective method for power quality (PQ) disturbance recognition under the challenges of increasing power system pollution. This paper proposes a PQ disturbance signal recognition method based on Multiresolution S transform (MST) and decision tree (DT). For improving recognition accuracy, adjustment factors are introduced to obtain a controllable time-frequency resolution. On this basis, five feature statistics are obtained to quantitatively reflect the characteristics of the analyzed power quality disturbance signals, which is less than the traditional S-transform-based method. As the proposed methodology can effectively identify the PQ disturbances, the efficiency of the DT classifier could be guaranteed. In addition, the noise impacts are also taken into consideration, and 16 types of noisy PQ signals with a signal-to-noise ratio (SNR) scoping from 30 to 50 dB are used as the analyzed dataset. Finally, a comparison between the proposed method and other popular recognition algorithms is conducted. The experimental results demonstrate that the proposed method is effective in terms of detection accuracy, especially for combined PQ disturbances.

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Power Quality Disturbance Recognition Based on Multiresolution S-Transform and Decision Tree

An algorithm making use of hybrid features of Hilbert transform (HT) and Stockwell transform (ST) to identify the single-stage and multiple (multi-stage) power quality disturbances (PQDs) is introduced in this manuscript. A power quality index (PI) and time location index (TLI), based on the features computed from the voltage signal by the use of HT and ST are proposed for recognition of the PQDs. Four features extracted from the PI and TLI are considered for classification of the PQDs achieved using decision tree driven by rules. The algorithm is tested on the PQDs generated with the help of mathematical models (in conformity with standard IEEE-1159). Performance is evaluated on 100 data set of every disturbance computed by varying various parameters, and efficiency is found to be greater than 99%. It is established that an algorithm is effective for recognition of PQ events with an efficiency greater than 98% even in the presence of high-level noise. Algorithm is faster compared to many reported techniques and scalable for application to voltages of all range. Results are validated through comparison with the results of the algorithms reported in the literature. Performance of the algorithm is effectively validated on the practical utility network. This algorithm can be effectively implemented for designing the power quality (PQ) monitoring devices for the utility grids.

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A Hybrid Algorithm for Recognition of Power Quality Disturbances

Transmission line fault detection is a complex, high-cost and time-consuming work for deep-sea offshore wind farms. Therefore, rapid intelligent on-line fault detection and classification of submarine transmission lines play a very important role in the offshore wind farm reliability improving and operating costs reduction. This paper presents a novel hybrid on-line detection method that combines Wavelet noise Reduction, Clarke transform, Stockwell transform and Decision Tree (WRC-SDT). First, the measured Wind Turbine (WT) voltage signals are processed through wavelet noise reduction and Clarke transform to get the gradient of the voltage component. The gradient of the voltage component is then monitored in order to detect faults. Second, the recorded WT current date are processed through Stockwell transform with a view to obtaining the transmission line fault eigenvalues. The fault eigenvalues are then taken as the input of decision tree in order to classify different types of faults. To verify the feasibility and performance of the proposed method, the comparison of a detection and classification result is presented based on more than 1600 fault simulation data. The results show that WRC-SDT method is immune to fault resistance, starting angle and location. The proposed method is also robust to measurement noise.

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WRC-SDT Based On-Line Detection Method for Offshore Wind Farm Transmission Line

In this paper, a novel method for phase selection of faults during power swing is presented. The proposed method operates with current signals from one end of the protected transmission line. The most popular and powerful ensemble machine learning technique Random Forest is used for fault classification. The magnitude of the three-phase incremental quantities of currents is used as input to random forest for classifying the fault. The incremental quantity of the current signal is computed using a moving average filter. The proposed method is validated with 9216 cases covering varying swing frequency, inception angle, fault type, fault resistance, source to line impedance ratio and fault distance. Achieved fault classification within 10msec with an accuracy of 99.8%.

An Ensemble Machine Learning Based Fault Classification Method for Faults During Power Swing

The voltage sags’ cause recognition is the basis for formulating governance plans and clarifying liabilities for accidents. For voltage sag cause recognition methods based on physical characteristics, new challenges are presented in terms of accuracy, adaptability and algorithm efficiency. Deep learning is a method based on characterizing and learning data. The efficient mechanism of autonomous feature learning can effectively overcome the problems of information loss and generalization ability based on existing physical property methods. The long short-term memory network (LSTM) has the characteristics of memory and can better learn the data characteristics with time series characteristics. Bidirectional LSTM can consider historical information and future information compared with standard LSTM, and has more processing for time series data. While using the attention mechanism can highlight the key influencing factors in the time series and improve the recognition accuracy of the model. For the transient sag time series data, this paper proposes a multi-layer structure based on bidirectional LSTM and attention mechanism to classification recognition. The experiment uses simulation data and measured data to prove that the model has good recognition ability and good anti-noise performance in the recognition of voltage sag causes, and can be reliably applied in practical engineering.

Method of Voltage Sag Causes Based on Bidirectional LSTM and Attention Mechanism

This manuscript describes an algorithm based on Stockwell Transform and rule based decision tree for detection and classification of complex power quality disturbances. The multiple (complex) power quality (PQ) events are generated by various combinations of single stage PQ disturbances. These complex PQ disturbances are generated in conformity with IEEE Standard-1159 using the mathematical relations in MATLAB as per various combinations of voltage swell, voltage sag, momentary interruption (MI), oscillatory transient (OT), harmonics and impulsive transient (IT). These complex power quality signals are decomposed with the help of Stockwell transform to obtain S-matrix. Various plots are obtained from the S-matrix. Various features are extracted from these plots and given as input to the rule-based decision tree for classification of complex PQ disturbances. The effectiveness of proposed algorithm is established by testing 30 data sets of each complex PQ disturbance obtained by varying the parameters.

Detection and Classification of Complex Power Quality Disturbances Using Stockwell Transform and Rule Based Decision Tree

This paper presents an algorithm for detection and classification of different types of the power system faults occurring on the transmission line to provide the effective protection to the line against these faults. The proposed technique uses Stockwell transform based multi-resolution analysis of the current signals for detection of the faults and rule based decision tree for classification of faults in the power system network. Faults investigated in this research work include line to ground (LG) fault, double line (LL) fault, double line to ground (LLG) fault and three phase fault with the involvement of the ground (LLLG). Proposed study is performed in MATLAB/Simulink environment.

Detection and Classification of Transmission Line Faults Using Stockwell Transform and Rule Based Decision Tree

This paper is focussed on restructuring of existing transmission network in the Barmer, Jaisalmer and Jodhpur districts of Rajasthan to meet out load demand of the refinery and petrochemical complex at Pachpadra. Different options have been analyzed and compared with existing power system network of Rajasthan in terms of losses to find out optimized solution for load demand of refinery. Study is performed using the MiPower software. Transmission network of Rajasthan considering interstate connections have been modelled in MiPower software. Various options are tested with different scenarios of renewable energy (RE) generation because in western Rajasthan the RE penetration in the utility grid is high. Short circuit study of various proposals has been compared with base case to investigate impact of restructuring on protection systems. An optimized solution is suggested to cater load demand of refinery and petrochemical complex with high reliability of power.

Optimized Approach for Restructuring of Rajasthan Transmission Network to Cater Load Demand of Rajasthan Refinery and Petrochemical Complex

This paper presents an algorithm to provide current based scheme for protection of transmission line. The proposed algorithm is implemented using harmonic wavelet transform (HWT) and rule based decision tree. Current measured on two ends of transmission line has been decomposed using harmonic wavelet transform. Absolute values of output of harmonic wavelet transform based decomposition of current signal is taken as fault index and plotted for recognition of faults. Peak values of the fault indexes are used as input data to rule based decision tree to classify faults. Proposed current based scheme proved to be effective in detection and classification of transmission line faults to provide efficient protection to line.

Mahendra Kumar

Papers

Detection and Classification of Complex Power Quality Disturbances Using Stockwell Transform and Rule Based Decision Tree

Detection and Classification of Transmission Line Faults Using Stockwell Transform and Rule Based Decision Tree

Optimized Approach for Restructuring of Rajasthan Transmission Network to Cater Load Demand of Rajasthan Refinery and Petrochemical Complex

A Current Based Algorithm Using Harmonic Wavelet Transform and Rule Based Decision Tree for Transmission Line Protection