An improved fault-location method for distribution system using wavelets and support vector regression

This paper presents an artificial neural network (ANN) and support vector machine (SVM) approach for locating faults in radial distribution systems. Different from the traditional Fault Section Estimation methods, the proposed approach uses measurements available at the substation, circuit breaker and relay statuses. The data is analyzed using the principal component analysis (PCA) technique and the faults are classified according to the reactances of their path using a combination of support vector classifiers (SVCs) and feedforward neural networks (FFNNs). A practical 52 bus distribution system with loads is considered for studies, and the results presented show that the proposed approach of fault location gives accurate results in terms of the estimated fault location. Practical situations in distribution systems, such as protective devices placed only at the substation, all types of faults, and a wide range of varying short circuit levels, are considered for studies. The results demonstrate the feasibility of applying the proposed method in practical distribution system fault diagnosis.

Fault location identification is an important task to provide reliable service to the customer. Most existing artificial intelligence methods such as neural network, fuzzy logic, and support vector machine (SVM) focus on identifying the fault type, section, and distance separately. Furthermore, studies on fault type identification are focused on overhead transmission systems and not on underground distribution systems. In this paper, a fault location method in the distribution system is proposed using SVM, addressing the limitations of existing methods. Support vector classification (SVC) and regression analysis are performed to locate the fault. The method uses the voltage sag data during a fault measured at the primary substation. The type of fault is identified using SVC. The fault resistance and the voltage sag for the estimated fault resistance are identified using support vector regression (SVR) analysis. The possible faulty sections are identified from the estimated voltage sag data and ranked using the Euclidean distance approach. The proposed method identifies the fault distance using SVR analysis. The performance of the proposed method is analyzed using Malaysian distribution system of 40 buses. Test results show that the proposed method gives reliable fault location.

An approach of a mean hourly wind speed forecasting in wind farm is proposed in this paper. It applies support vector regression as well as forecasting error estimation. Firstly, support vector regression is applied to the mean hourly wind speed forecasting. Secondly, a support vector classifier is trained to estimate the forecasting error. Finally, the forecasting results can tailor themselves to the estimated forecasting error, and thus improve the forecasting precision. To test the approach, three-year data from a wind farm is given as a support vector regression process, and a support vector classifier is trained in addition to estimate the forecasting error. Experimental results show that the proposed approach can achieve higher quality of mean hourly wind speed forecasting; also it has lower mean square error compared with the traditional support vector regression forecasting.

The rising demand for stable power supply in distribution systems has increased the importance of reliable supply. Thus, a networked distribution system (NDS) linked with individual lines is being adopted, gradually replacing the radial distribution system (RDS) currently applied to most distribution systems. Implementing the NDS can lead to various improvements in factors such as line utilization rate, acceptance rates of distributed power, and terminal voltages, while mitigating line losses. However, compared with the RDS, the NDS can experience bidirectional fault currents owing to its interconnected lines, thereby hindering protection coordination, which must be addressed before the NDS can be implemented in real-world power systems. Due to the characteristics of NDS, the reverse fault current is relatively small. However, this phenomenon becomes more severe when the high impedance fault (HIF) occurs. In this paper, the malfunction of protective devices during the HIF is directly verified and analyzed in the NDS. As a result, when the HIF occurs, the issue of the reverse protective device malfunctioning worsens because of a reduction in fault current and a failure in direction detection. To solve this issue, this work proposes a communication-based protection algorithm. Through the comparative verification of the proposed algorithm and the conventional protection method, protection coordination can be secured in the case of an HIF without new devices. It must be highlighted that the proposed method does not affect the settings of the protective device and provides a cost-effective and efficient solution since this algorithm is added independently to the existing relay.

A rule-based expert system designed to locate the faults in a distribution system is presented. Distribution system component data and network topology are stored in the database. A set of heuristic rules is compiled from the dispatcher's experience and imbedded in the rule base. To locate distribution system faults, an inference engine is developed to perform deductive reasoning on the rules in the knowledge base. The inference engine comprises three major parts: the dynamic searching method, the backtracking approach, and the set intersection operation. The expert system is implemented on a personal computer using the artificial intelligence language Prolog. To demonstrate the effectiveness of the proposed approach, the expert system has been used to locate faults in a real underground distribution system within the service area of the Taipei City District Office of the Taiwan Power Company. It is found that the expert system can identify fault location very efficiently. Therefore, it can serve as a valuable tool to help distribution system dispatchers determine fault locations.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

This study aims to develop a real-time model for detecting and classifying various fault types in power distribution networks to enhance reliability and operational efficiency. A hybrid DWT–SVM approach is essential for accurately detecting and classifying faults in modern power distribution systems, especially under non-stationary and dynamic conditions. The proposed method addresses a range of fault scenarios, including single-phase-to-ground, line-to-line, double-line-to-ground, and three-phase faults, within a 33 kV distribution network. A hybrid approach combining Discrete Wavelet Transform (DWT) and Support Vector Machine (SVM) is introduced. Using the Debauchies-4 (Db4) wavelet, DWT effectively decomposes transient fault currents at the source terminal, capturing critical time-frequency domain features. Fault classification is performed using an SVM optimized with a Radial Basis Function (RBF) kernel and Particle Swarm Optimization (PSO), enabling precise mapping of data into higher-dimensional spaces for optimal separation. Validation conducted with MATLAB R2023b demonstrates a detection accuracy of 100% and a classification accuracy of 99%. Comparative analyses against models such as PSO-based Support Vector Machine (PSO-SVM), DWT-DNN and wavelet transform with artificial neural networks (WT-ANN) on the IEEE 13-bus system highlight the proposed method's superior performance. This innovative approach proves to be robust, adaptable, and highly effective across diverse fault scenarios, offering significant improvements in accuracy and reliability for fault detection and classification in power distribution networks. The method supports real-time operation by using high-speed simulation platforms MATLAB/Simulink to process signals and classify faults within milliseconds using a pre-trained SVM model.

The Differential Protection Scheme for Microgrid Using S-Transform

High‐impedance faults (HIFs) in electrical power distribution systems produce a very random, non‐linear and low‐magnitude fault current. The conventional overcurrent (OC) relaying‐based distribution system protection schemes find difficulty in detecting such low‐current HIFs. In this study, a simple two criteria‐based protection scheme is proposed for detection and isolation of HIFs in multi‐feeder radial distribution systems. It utilises one‐cycle sum of superimposed components of residual voltage for HIF detection and the maximum value of one‐cycle sum of superimposed components of negative‐sequence current for faulted feeder identification. The performance of the proposed scheme is evaluated for a wide variety of possible test cases by generating data through power systems computer‐aided design/electro‐magnetic transient design and control software. Results clearly show that the proposed scheme can assist conventional OC relay for detection and isolation of HIFs in distribution systems with any grounding connections in a more reliable and faster way.

Identification and characterization of flow structures in chemical process equipment using multiresolution techniques

Electrical distribution system, which is the largest portion of networks, acts as a final power delivery path to the end users. Hence, ensuring its efficient and reliable operation is an extremely important and challenging task. Utilities, all over the world are competing for improvement in service to consumers, and the area of fault detection and diagnosis in distribution systems have attracted the significant attention of researchers in last few decades. With availability of inadequate system information and presence of high impedance faults, locating faults in a distribution system pose a major challenge to the utility operators. Most of the previous work on fault identification and location concentrated on estimating the status of circuit breakers and relays with aid of some algorithmic approaches. In this paper, a fault identification and location technique using wavelet multi-resolution approach for radial distribution systems are proposed. The current measurements at the substation, available in the distribution network have been utilized and the effectiveness of the proposed approach is demonstrated on 7-node and 19-node three-phase test systems.

Surface morphologies of supported polyethylene (PE) catalysts are investigated by an approach combining fractal with wavelet. The multiscale edge (detail) pictures of catalyst surface are extracted by wavelet transform modulus maxima (WTMM) method. And, the distribution of edge points on the edge image at every scale is studied with fractal and multifractal method. Furthermore, the singularity intensity distribution of edge points in the PE catalyst is analyzed by multifractal spectrum based on WTMM. The results reveal that the fractal dimension values and multifractal spectrums of edge images at small scales have a good relation with the activity and surface morphology of PE catalyst. Meanwhile the catalyst exhibiting the higher activity shows the wider singular strength span of multifractal spectrum based on WTMM, as well as the more edge points with the higher singular intensity. The research on catalyst surface morphology with hybrid fractal and wavelet method exerts the superiorities of wavelet and fractal theories and offers a thought for studying solid surfaces morphologies.

The prediction of mold level is a basic and key problem of continuous casting production control. Many current techniques fail to predict the mold level because of mold level is non-linear, non-stationary and does not have a normal distribution. A hybrid model, based on empirical mode decomposition (EMD) and support vector regression (SVR), is proposed to solve the mold level in this paper. Firstly, the EMD algorithm, with adaptive decomposition, is used to decompose the original mold level signal to many intrinsic mode functions (IMFs). Then, the SVR model optimized by genetic algorithm (GA) is used to predict the IMFs and residual sequences. Finally, the equalization of the predict results is reconstructed to obtain the predict result. Several hybrid predicting methods such as EMD and autoregressive moving average model (ARMA), EMD and SVR, wavelet transform (WT) and ARMA, WT and SVR are discussed and compared in this paper. These methods are applied to mold level prediction, the experimental results show that the proposed hybrid method based on EMD and SVR is a powerful tool for solving complex time series prediction. In view of the excellent generalization ability of the EMD, it is believed that the hybrid algorithm of EMD and SVR is the best model for mold level predict among the six methods, providing a new idea for guiding continuous casting process improvement.

Accurate fault location information helps operator and utility personnel to expedite service restoration, reduce outage time, operating cost and customer complaints. The power system automation lab (PSAL) at Texas A & M University has developed three modules to locate faults in radial distribution systems. These modules use a fuzzy logic approach in which each module computes a possibility value for each line section in a distribution system. Possibility values represent the possibility a line section has a fault. By aggregating these possibility values, the final possibility value for each section can be computed. In order to develop an aggregation method and verify the validity of the three modules, data that represent currents and voltages measured at the substation of a radial distribution system during fault conditions are needed. Also system information such as equipment parameters, protective device settings and phase distribution of line sections must be known. Because this information is not publicly available from a real system, generating this data through simulation of a radial distribution system with protective devices is required. One difficulty in this simulation is the lack of models in existing circuit simulation software such as ATP and MATLAB for protective devices controlled by time-current characteristics (TCC). This paper proposes a method to model TCC-based protective devices. SIMULINK and SimPowerSystems (SPS) blockset of MATLAB were used to model the system. S-functions, special type functions that interact with SIMULINK's equation solvers, were used to model the protective device control logic. The IEEE 34-bus test feeder was used to show the validity of the modeling method. The results show the modeling methodology works accurately.

A new corner detection method for contour images is proposed based on dyadic wavelet transform (WT). The wavelet transform modulus maxima (WTMM) at different scales are taken as corner candidates. For each candidate, the scale at which the maximum value of the WTMM exists is defined as its local natural scale, and the corresponding modulus is taken as the significance measurement. This approach achieves more accurate estimations of the natural scale than the existing global natural scale methods. The simulation results show that the proposed method is effective for both long contours and short contours. The objective evaluation reveals the better performance of the proposed algorithm compared to the existing methods. The technique is inherently fast due to the fast implementations of the dyadic WT computations

Comparative analysis of machine learning models for rainfall prediction