Analysis of drawbacks and constraints of classification algorithms for three-phase voltage dips

Voltage events are one of the most common and harmful disturbances of power electric systems. Voltage dips, swells and interruptions are included under this heading. Given the economic cost that these disturbances represent for electrical power transmission and distribution companies and the industry, it becomes imperative to detect and classify them properly. Several classification criteria and algorithms have been proposed in the literature as analysis tools to differentiate voltage events by their characteristics and, if possible, to determine their causes and consequences. Even though some of these approaches make a correct classification of the voltage events, there are certain operation conditions that are common in real electrical grids, in which the classification criteria, and their corresponding algorithms, make a wrong classification. These particular conditions, together with the lack of a fair comparison in a common scenario, have not been addressed in the specific field literature. This work explores in detail all these aspects by evaluating the symmetrical components criterion and ABC classification criterion, and rigorously analyzes three specific algorithms: the Symmetrical Components Algorithm, the Six Phases Algorithm and the Space Vector Algorithm. Drawbacks arise from both classification criteria and algorithms. The causes of the classification errors are described and discussed in detail in order to better understand the problem, and evidence the constraints of these classification methods.Fil: Strack, Jorge Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica; ArgentinaFil: Carugati, Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica; ArgentinaFil: Orallo, Carlos Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica; ArgentinaFil: Donato, Patricio Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica; ArgentinaFil: Maestri, Sebastian Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica; ArgentinaFil: Carrica, Daniel Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica; Argentin

A Review of Fault Diagnosing Methods in Power Transmission Systems

Transient stability is important in power systems. Disturbances like faults need to be segregated to restore transient stability. A comprehensive review of fault diagnosing methods in the power transmission system is presented in this paper. Typically, voltage and current samples are deployed for analysis. Three tasks/topics; fault detection, classification, and location are presented separately to convey a more logical and comprehensive understanding of the concepts. Feature extractions, transformations with dimensionality reduction methods are discussed. Fault classification and location techniques largely use artificial intelligence (AI) and signal processing methods. After the discussion of overall methods and concepts, advancements and future aspects are discussed. Generalized strengths and weaknesses of different AI and machine learning-based algorithms are assessed. A comparison of different fault detection, classification, and location methods is also presented considering features, inputs, complexity, system used and results. This paper may serve as a guideline for the researchers to understand different methods and techniques in this field

Detection and Location of Faults in Wide Area Systems based on Error-Dependent Communication Strategy

Transmission system serves as a crucial link between generating stations and consumers. Early detection and accurate location of faults on transmission lines are essential to prevent the occurrence of blackouts. Also real time monitoring of power system states during faults will enhance the situational awareness for power system operators. Wide Area Measurement and Protection Systems (WAMPS) based on Phasor Measurement Unit (PMU) are a promising solution for dynamic real time monitoring and protection of power system.;This thesis deals with detection and location of faults on a transmission system based on synchrophasor technology. Performance of WAMPS is largely dependent on the performance of its information and communication technologies infrastructure. Error-dependent communication strategy is employed in this work for communication of real time data from PMU to the centralized controller. As PMUs are expensive, they cannot be placed at every bus. Hence linear state estimator based on synchronized measurements is employed for estimating the state of the entire system. The estimated states of the system are then compared to a certain threshold and if any abnormality is found, fault is detected. Once the faulted bus is detected, two-terminal algorithm is employed to identify the exact location of fault. The proposed methodology is implemented on IEEE 9 bus system developed in MATLAB/SIMULINK environment

Accurate Location of Evolving Faults on Transmission Lines Using Sparse Wide Area Measurements

In electric power systems, not all fault conditions remain unchanged during faults. An evolving fault has one characteristic initially and changes to a different condition subsequently. Locating evolving faults is challenging due to the change in fault type shortly after the fault initiation. This paper presents a new approach for estimating the locations of evolving faults on transmission lines. By using sparse wide area voltage measurements, this method is able to accurately locate evolving faults without requiring measurements from either end of the faulted line. There is no need to detect whether a fault is an evolving fault or not. Fault type information is not a necessity either, and the change of fault phases does not affect the estimation accuracy. In addition, the algorithm is applicable to both single-circuit and double-circuit lines, and the transmission lines can be either transposed or untransposed. Distributed parameter line model is adopted to fully consider the shunt capacitances of the transmission lines. Electromagnetic Transient Program (EMTP) is employed to simulate transmission system, and quite accurate results have been achieved

A Comparison of Two Methods Using R.M.S. Value and Wavelet-Based Mann and Morrison Algorithm for Voltage Dip Characterization

Voltage dips have been identified as the major problem among power quality disturbance events which affect the industrial customers. The dips involve short reduction in Root Mean Square (RMS) voltage caused by fuults in electrical supply system or starting of a large load. It is crucial to measure and analyze the voltage dip events before considering any mitigation action to reduce and eliminate it. This work presents comparison of two methods used for voltage dip detection and characterization. A less complicated method performed is to determine the lowest magnitude of the RMS voltage during the disturbance. Another method is a combination of wavelet-based analysis using Mann and Morrison algorithm which estimates the amplitude and the phase angle to characterize the dips. The performances of both methods are examined using simulation of system voltage dip, duration of the dip occurrences and point-on wave at beginning in a sinusoidal voltage supply. The voltage dips are then being classified into some classes according to their characteristics. The numerical approach using RMS value results in simplicity method which is easy to be implemented and understood. Eventhough the wavelet-based method seems to be more complicated, it provides higher accuracy in determining the voltage dip characterization. ii

Analysis of Line Outage Detection in Nigeria 330kV Transmission Lines using Phasor Measurement Units

In this work, an analysis of line outage detection in Nigeria 330kV transmission lines using Phasor Measurement Units was presented. This requires collection and analysis of the data obtained from Transmission Company of Nigeria with the aid of PSAT 2.10.1 / MATLAB SIMULINK using Newton-Raphson power flow algorithm and also to determine the effectiveness of PMU when introduced in our power system network. 12 buses and 3 Generators system were considered for the studied. This was achieved by collecting relevant transmission parameters for 330kV line and was simulated on PSAT 2.10.1 and MATLAB 2015a using Newton-Raphson power flow algorithm. The work involved an offline and online analysis. For the offline analysis the admittance / impedance matrix for Y-bus and bus voltage for pre-outage was obtained via the power flow analysis and change in impedance for the lines were calculated. These values were further normalised in order to reduce the value to a row echelon form. Then for the online analysis; the change in phase angle from the Phasor Measurement Unit (PMU) online simulation for pre-outage and also post-outage was calculated and a normalised column matrix was gotten. Finally, the effectiveness of the line outage detection was graphically represented using MATLAB software to plot the values of the normalised values of the offline and online analysis; i.e., by comparing the normalised form of the offline and online values. These results clearly show that PMUs gives an accurate monitoring and total observability when introduced in Nigeria power system

Fault Location and Incipient Fault Detection in Distribution Cables

A set of fault location algorithms for underground medium voltage cables, two incipient fault detection schemes for distribution cables and a state estimation method for underground distribution networks are developed in this thesis. Two schemes are designed to detect and classify incipient faults in underground distribution cables. Based on the methodology of wavelet analysis, one scheme is to detect the fault-induced transients, and therefore identify the incipient faults. Based on the analysis of the superimposed fault current and negative sequence current in time domain, the other scheme is particularly suitable to detect the single-line-to-ground incipient faults, which are mostly occurring in underground cables. To verify the effectiveness and functionalities of the proposed detection algorithms, different fault conditions, various system configurations, real field cases and normal operating transients are examined. The simulation results have demonstrated a technical feasibility for practical implementations of both schemes. Based on the methodology of the direct circuit analysis, a set of location algorithms is proposed to locate the single phase related faults in the typical underground medium voltage cables. A large number of complex nonlinear equations are effectively solved to find the fault distance and fault resistance. The algorithms only utilize the fundamental phasors of three-phase voltages and currents recorded at single end, normally at substation. The various system and fault conditions are taken into account in the development of algorithms, such as effects of shunt capacitance, mutual effects of metallic sheaths, common sheath bonding methods and different fault scenarios. The extensive simulations have validated the accuracy and effectiveness of the proposed algorithms. In order to extend the proposed fault location algorithms to underground distribution networks, a state estimation algorithm is developed to provide the necessary information for the location algorithms. Taking account of the complexity and particularity of cable circuits, the problem of the state estimation is formulated as a nonlinear optimization problem that is solved by the sequential quadratic programming technique. The simulation studies have indicated that the proposed fault location scheme incorporating with the state estimation algorithm can achieve good performance under different load and fault conditions

Transient stability assessment of hybrid distributed generation using computational intelligence approaches

Includes bibliographical references.Due to increasing integration of new technologies into the grid such as hybrid electric vehicles, distributed generations, power electronic interface circuits, advanced controllers etc., the present power system network is now more complex than in the past. Consequently, the recent rate of blackouts recorded in some parts of the world indicates that the power system is stressed. The real time/online monitoring and prediction of stability limit is needed to prevent future blackouts. In the last decade, Distributed Generators (DGs) among other technologies have received increasing attention. This is because DGs have the capability to meet peak demand, reduce losses, due to proximity to consumers and produce clean energy and thus reduce the production of CO₂. More benefits can be obtained when two or more DGs are combined together to form what is known as Hybrid Distributed Generation (HDG). The challenge with hybrid distributed generation (HDG) powered by intermittent renewable energy sources such as solar PV, wind turbine and small hydro power is that the system is more vulnerable to instabilities compared to single renewable energy source DG. This is because of the intermittent nature of the renewable energy sources and the complex interaction between the DGs and the distribution network. Due to the complexity and the stress level of the present power system network, real time/online monitoring and prediction of stability limits is becoming an essential and important part of present day control centres. Up to now, research on the impact of HDG on the transient stability is very limited. Generally, to perform transient stability assessment, an analytical approach is often used. The analytical approach requires a large volume of data, detailed mathematical equations and the understanding of the dynamics of the system. Due to the unavailability of accurate mathematical equations for most dynamic systems, and given the large volume of data required, the analytical method is inadequate and time consuming. Moreover, it requires long simulation time to assess the stability limits of the system. Therefore, the analytical approach is inadequate to handle real time operation of power system. In order to carry out real time transient stability assessment under an increasing nonlinear and time varying dynamics, fast scalable and dynamic algorithms are required. Transient Stability Assessment Of Hybrid Distributed Generation Using Computational Intelligence Approaches These algorithms must be able to perform advanced monitoring, decision making, forecasting, control and optimization. Computational Intelligence (CI) based algorithm such as neural networks coupled with Wide Area Monitoring System (WAMS) such as Phasor Measurement Unit (PMUs) have been shown to successfully model non-linear dynamics and predict stability limits in real time. To cope with the shortcoming of the analytical approach, a computational intelligence method based on Artificial Neural Networks (ANNs) was developed in this thesis to assess transient stability in real time. Appropriate data related to the hybrid generation (i.e., Solar PV, wind generator, small hydropower) were generated using the analytical approach for the training and testing of the ANN models. In addition, PMUs integrated in Real Time Digital Simulator (RTDS) were used to gather data for the real time training of the ANNs and the prediction of the Critical Clearing Time (CCT)

Design and implementation of ANN based phase comparators applied to transmission line protection

There has been significant development in the area of neural network based power system protection in the previous decade. Neural network technology has been applied for various protective relaying functions including distance protection. The reliability and efficiency of ANN based distance relays is improving with the developing digital technologies. There are, however, some inherent deficiencies that still exist in the way these relays are designed. This research addresses some of these issues and proposes an improved protective relaying scheme. The traditional ANN distance relay designs use parameter estimation algorithms to determine the phasors of currents and voltages. These phasors are used as inputs to determine the distance of a fault from relay location. The relays are trained and tested on this criterion; however, no specific relay characteristic has been defined. There is a need for development of a new methodology that will enable designing of an ANN that works as a generic distance relay with clearly defined operating boundary. This research work presents a modified distance relaying algorithm that has been combined with a neural network approach to eliminate the use of phasors. The neural network is trained to recognize faults on basis of a specific relay characteristic. The algorithm is flexible and has been extended for the design of other relays. The neural network has been trained using pure sinusoidal values and has been tested on a 17-bus power system simulated in PSCAD. The training and testing of the neural network on different systems ensures that the relay is generic in nature. The proposed relay can be used on any transmission line without re-training the neural network. The design has been tested for different fault conditions including different fault resistances and fault inception angles. The test results show that the relay is able to detect faults in lesser time as compared to conventional relay algorithms while maintaining the integrity of relay boundaries