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Fault detection and isolation for low voltage distribution grids with distributed generation
Abstract
New smart meters, distributed generation, renewable energy sources and the concern about the environment are redefining the way to conceive and operate electrical grids. To take full advantage of the new electrical smart grids we need to monitor and protect them. The capability of self-healing is thus important in smart grids in order to ensure a proper behavior under faults and reduce power outage times. For this purpose, this thesis proposes three different methods of fault diagnosis for low voltage (LV) distribution grids and two methods of fault isolation for grid-connected photovoltaic systems (GCPVs).In electrical power distribution systems, faults are responsible for 80% of customer interruptions. While several fault location methods for distribution grids exist in the literature, the majority of them focuses on medium voltage grids and low fault resistance values that rarely surpass the 100 Ohms. Taking into account that distribution system operators usually rely on phone calls to detect and locate faults in LV grids, the need for fault detection and location techniques that cover these cases, i.e. large fault resistances and LV distribution grids, is evident.The three fault detection and location methods proposed in this thesis are:1.A conventional fault detection method based on overcurrent monitoring in combination with a method that uses sparse voltage measurements to build the voltage profile across the faulty branch for fault location.2.Gradient boosting trees(GBT), a method that has been proven to excel in many applications the last few years.3.Deep neural networks(DNN), a method that improve the traditional neural network architecture by taking advantage of an increased number of hidden layers.Simulations on a real semi-rural LV distribution grid of Portugal are performed to validate the results. A common case study is used to compare the three methods. The influencing parameters are: a) a big variety of fault resistance values (63,772 values between 1 and 1000 Ohms), b) nine different fault locations within each sector, c) two fault types (single phase to ground and three phase faults), d) a simultaneity factor of 0.5, e) a big spectrum of PV generation and load demand scenarios with 70,334 studied combinations and f) a 2% underestimation error in measurements.Overall, DNN are the most reliable solution demonstrating a 100% accuracy in fault detection and an average of 12% of error in distance estimation. Moreover, under the minimum available measurements (on at the beginning of the feeder and one at each terminal node) case their accuracy is decreased by only 4.5%.At the same time, faults in PV generators, present an increased interest as well. A big variety of faults can occur in a PV power plant. Based on their location faults can appear: a) in the PV array, b) in the power converters, c) on the dc bus and d) in the grid side. The development of fast, efficient and reliable fault detection and isolation methods for GCPVs, capable of dealing with the different types of faults, is a recognized necessity from the scientific community and a prerequisite for their integration in the smart grids.So far, to the author’s knowledge, no research has been found to monitor the GCPV as a complete system, i.e. isolating faults in every part of the plant with a single method. For this reason, two algorithms based on a signal approach are proposed as a fault isolation strategy. They use current and voltage measurements at the output of the inverter, examining faults occurring on all four of the aforementioned possible locations. The choice of the output of the inverter, i.e. the point of common coupling, as the monitoring source of the status of the GCPV system is in accordance with the location of voltage sensors used in the previous case of fault location methods in the LV distribution grid. Finally, the proposed algorithms achieve an isolation of 15 out of the 19 studied faults cases in less than 100 ms.
... In a recent research direction, machine learning (ML) techniques have been studied for fault detection in [21][22][23][24][25][26][27]. The study in [22] addresses the benefit of a machine learning framework for fault detection and classification in power systems. ...
... By analyzing the most used ML techniques (within consideration of fault types and metrics for those techniques evaluation), the authors have shown the benefits of supervised classifiers to reliably solve power system problems. In the same way, a part of the research in [23] was dedicated to the fault diagnosis in LV networks by using a deep neural networks approach. The results of this study allowed the authors to highlight the most influencing parameters in the fault assessment process, such as the fault resistance. ...
Low voltage distribution networks have not been traditionally designed to accommodate the large-scale integration of decentralized photovoltaic (PV) generations. The bidirectional power flows in existing networks resulting from the load demand and PV generation changes as well as the influence of ambient temperature led to voltage variations and increased the leakage current through the cable insulation. In this paper, a machine learning-based framework is implemented for the identification of cable degradation by using data from deployed smart meter (SM) measurements. Nodal voltage variations are supposed to be related to cable conditions (reduction of cable insulation thickness due to insulation wear) and to client net demand changes. Various machine learning techniques are applied for classification of nodal voltages according to the cable insulation conditions. Once trained according to the comprehensive generated datasets, the implemented techniques can classify new network operating points into a healthy or degraded cable condition with high accuracy in their predictions. The simulation results reveal that logistic regression and decision tree algorithms lead to a better prediction (with a 97.9% and 99.9% accuracy, respectively) result than the k-nearest neighbors (which reach only 76.7%). The proposed framework offers promising perspectives for the early identification of LV cable conditions by using SM measurements.
Power outages in electrical grids can have very negative economic and societal impacts rendering fault diagnosis paramount to their secure and reliable operation. In this paper, deep neural networks are proposed for fault detection and location in low-voltage smart distribution grids. Due to its key properties, the proposed method solves some of the drawbacks of the existing literature methods, namely a method that: 1) is not limited by the grid topology; 2) is branch-independent; 3) can localize faults even with limited data; 4) is the first to accurately detect and localize high-impedance faults in the low-voltage distribution grid. The generalizability of the method derives from the non-grid specific nature of the inputs that it requires, inputs that can be obtained from any grid. To evaluate the proposed method, a real low-voltage distribution grid in Portugal is considered and the robustness of the method is tested against several disturbances including large fault resistance values (up to 1000 Ω). Based on the case study, it is shown that the proposed methodology outperforms conventional fault diagnosis methods: it detects faults with 100% accuracy, identifies faulty branches with 83.5% accuracy, and estimates the exact fault location with an average error of less than 11.8%. Finally, it is also shown that: 1) even when reducing the available measurements to the bare minimum, the accuracy of the proposed method is only decreased by 4.5%; 2) while deep neural networks usually require large amounts of data, the proposed model is accurate even for small dataset sizes.
In this paper, a gradient boosting tree model is proposed to detect, identify and localize single-phase-to-ground and three-phase faults in low voltage (LV) smart distribution grids. The proposed method is based on gradient boosting trees and considers branch-independent input features to be generalizable and applicable to different grid topologies. Particularly, as it is shown, the method can be estimated in a specific grid topology and be employed in a different one. To test the algorithm, the method is evaluated in a simulated real LV distribution grid of Portugal. In this case study, different fault resistances, fault locations and hours of the day are considered. In detail, the algorithm is evaluated at eighteen fault resistance values between 0.1 and 1000 ; similarly, nine fault locations are considered within each one of the 32 sectors of the grid and the faults are simulated across different hours of a day. The developed algorithm showed promising results in both out-of-sample branch and fault resistance data especially for fault detection, demonstrating a maximum fault detection error of 0.72%.
This paper presents a real-time fault classification and location identification method for a smart distribution network using Artificial Neural Networks (ANN) and Advanced Metering Infrastructure (AMI). It also describes the development of a testbed for real-time testing of the proposed approach. The testbed consists of a simulated power system model (running on a digital real-time simulator (DRTS)) and AMI. The core parts of AMI are smart meters (SMs), a communication network (developed using DNP3 protocol over TCP/IP), data concentrator (DC) and a utility operations center (UOC). Event-driven data from the SMs are collected in the DC and then fed to the UOC for being used as inputs for the novel ANN based fault classification and location identification algorithm. Based on the data received, the algorithm can classify the type of fault and locate it with high accuracy. Both balanced and unbalanced fault types are tested on different nodes and lines throughout a distribution network modeled in offline and on the DRTS. A comprehensive sensitivity analysis is performed to validate the effectiveness of the proposed method by varying fault resistance, loading conditions, and noise level. Classification accuracy of over 99% is achieved when classifying all fault types, and more than 95% accuracy is achieved when identifying the location of the fault.
The environmentally clean nature of solar photovoltaic (PV) technology causes PV power generation to be embraced by all countries across the globe. Consequently, installation and utilization of PV power systems have seen much growth in recent years. Although PV arrays of such systems are robust, they are not immune to faults. To guarantee reliable power supply, economic returns, and safety of both humans and equipment, highly accurate fault detection, diagnosis, and interruption devices are required. In this paper, an overview of four major PV array faults and their causes are presented. Specifically, ground fault, line-line fault, arc fault, and hot spot fault have been covered. Next, conventional and advanced fault detection and diagnosis (FDD) techniques for managing these faults are reviewed. Moreover, a single evaluation metric has been proposed and utilized to evaluate the performances of the advanced FDD techniques. Finally, based on the papers reviewed, PV array fault management future trends and possible recommendations have been outlined.
The exact location of faults in the electrical distribution systems is a problem that affects not only the users, but also the companies providing the electric service. With greater time invested in this period, the losses due to unbilled energy and inconvenience to users increases, thus decreasing the quality of service. One of the causes of the growth in time is the misunderstanding that might exist in the localization systems that act under the presence of distributed generation sources in the distribution networks. In this sense, the present research develops an intelligent diagnosis of faults in distribution systems with distributed generation. Three stages are defined: Identification of the type of fault, the location of the zone, and the exact point of fault. A mixed method based on artificial intelligence and mathematical algorithms is applied. Eleven different types of faults that can occur in a distribution system are considered with six different values of fault resistances ranging from 5 to 30 . The errors found are less than 2% in the location of the fault point with robustness to variations in the load and the penetration of distributed generation.
Fault Diagnosis of Dynamic Systems provides readers with a glimpse into the fundamental issues and techniques of fault diagnosis used by Automatic Control (FDI) and Artificial Intelligence (DX) research communities. The book reviews the standard techniques and approaches widely used in both communities. It also contains benchmark examples and case studies that demonstrate how the same problem can be solved using the presented approaches. The book also introduces advanced fault diagnosis approaches that are currently still being researched, including methods for non-linear, hybrid, discrete-event and software/business systems, as well as, an introduction to prognosis.
Fault Diagnosis of Dynamic Systems is valuable source of information for researchers and engineers starting to work on fault diagnosis and willing to have a reference guide on the main concepts and standard approaches on fault diagnosis. Readers with experience on one of the two main communities will also find it useful to learn the fundamental concepts of the other community and the synergies between them. The book is also open to researchers or academics who are already familiar with the standard approaches, since they will find a collection of advanced approaches with more specific and advanced topics or with application to different domains. Finally, engineers and researchers looking for transferable fault diagnosis methods will also find useful insights in the book.
This paper proposes an improved impedance based fault location scheme based on system analysis at non-fundamental frequencies. The fault is treated as a voltage source that injects high frequency components into the system and the analysis is carried out using these injected components. The proposed method only requires local measurements at the substation and therefore is classified as a single end method. The new contribution is that the proposed method uses the distributed parameter line model to account for inductive and capacitive effects of the line. It has been evaluated on the IEEE 34-bus feeder which is based on an actual distribution system which has the typical features such as non-homogeneous feeder sections, asymmetrical line configurations, unbalanced loads and single and three-phase laterals. The fault point, fault resistance and fault inception angle have been varied to check their influence on the accuracy of the method. The simulation results demonstrate the accuracy of the proposed method where for most cases, the error in fault location is less than 50 m.
Power System SCADA and Smart Grids brings together in one concise volume the fundamentals and possible application functions of power system supervisory control and data acquisition (SCADA). The text begins by providing an overview of SCADA systems, evolution, and use in power systems and the data acquisition process. It then describes the components of SCADA systems, from the legacy remote terminal units (RTUs) to the latest intelligent electronic devices (IEDs), data concentrators, and master stations, as well as: • Examines the building and practical implementation of different SCADA systems • Offers a comprehensive discussion of the data communication, protocols, and media usage • Covers substation automation (SA), which forms the basis for transmission, distribution, and customer automation • Addresses distribution automation and distribution management systems (DA/DMS) and energy management systems (EMS) for transmission control centers • Discusses smart distribution, smart transmission, and smart grid solutions such as smart homes with home energy management systems (HEMs), plugged hybrid electric vehicles, and more Power System SCADA and Smart Grids is designed to assist electrical engineering students, researchers, and practitioners alike in acquiring a solid understanding of SCADA systems and application functions in generation, transmission, and distribution systems, which are evolving day by day, to help them adapt to new challenges effortlessly. The book reveals the inner secrets of SCADA systems, unveils the potential of the smart grid, and inspires more minds to get involved in the development process.