Fault analysis on power systems using wavelet transformed transients and artificial intelligence

CHAPTER 1 INTRODUCTION 1.1 Overview Due to improvement in communication and technologies in power network, increasing number of system variables and alarms can be monitored and processed using the SCADA systems. As such, the expert system approach, which is based on knowledge and heuristics rules obtained from experts, has been widely used in power system operation for its reliability [1][2]. Due to the limitations of present expert systems, there are persistent problems and difficulties. One of the most difficult issues in fault diagnosis is the location of multiple faults and faults in unusual network configuration, such as a T-branch. They may not be precisely diagnosed due to the large number of fault candidates [2][3]. For example, a typical ring configuration, where a large section has tripped, the multiple fault locations can only be shown in a global manner (e.g. “Fault somewhere between these two regions A and B” or “ Possible fault between these two regions A and B”). For radial configuration, multiple faults in the same radial path cannot be diagnosed at all. Secondly, with the increase in the network size and the knowledge base for the inference system, an expert system may be inadvertently slowed down even in the case of simple faults, especially with the ‘firing’ of unnecessary rules. This is further complicated by situations where the protection devices falsely operated or did not work at all. These issues represent a widespread and constant problem in power utilities, such as Singapore Power, which also employs an expert system for its energy management systems (EMS). 1 A number of solutions have been proposed from installing fault detection systems at the network to improve network data precision to increasing the range of input data types. Much research progress have been achieved especially with more advanced concepts that took into account the time sequence of the circuit breaker during switching [4], restorative data of the network and even deep reasoning or model-based reasoning systems [5]. They, however, have very serious limitations in terms of reliability, robustness, and most importantly, speed. For time sequence of the circuit breaker switching, the highly random nature and unreliability of the inputs will have a limiting effect on its commercial practicality and reliability of such a system. As for the use of the restorative data of the network and an extra inference system for non-operating relays to reduce the search [3], its effectiveness is reduced significantly with multiple faults and loss or insufficient relay operating data due to malfunction relays or communication network failures. In other words, the system is intolerant towards lack of circuit breaker and relay status not available due to malfunctions, which is likely to increase if the fault area increases. For model–based reasoning systems, the choice of a proper and accurate representation is vital which may not be computationally efficient as the need for sophisticated reasoning mechanisms may be time-consuming [5]. With such inadequacy in the face of increasing need for precision, there is tremendous need to enhance accuracy and speed. The purpose of this work is, firstly, to design an effective fault diagnosis system for a typical distribution ring network in Singapore. Secondly, to propose the improving of the overall speed of the fault diagnosis process, by having a simple filter called the Intelligent Alarm Filter Processing (IAFP) to effectively reduce the work by first estimating the 2 region at fault. Thirdly, to verify the new concept of using wavelet transform (WT) values of transient fault currents and voltage, together with neural networks, at each ‘fault’ candidate to analyze and identify the precise locations of multiple faults. Fourthly, to test the effectiveness of a new form of ‘target’ neural network or small neural network trained to identify specific components. These neural networks are trained with a unique ‘target training’ process where they are trained with information for fault at that specific location only. They are independent of each other, only required to make simple decisions at each stage, and can be connected either in series or in parallel. Next, by training certain typical modules as “standard toolboxes” for each component, entire networks can be patched up by these target modules with a simple “ add-on” process to learn the characteristics for that location. This highly flexible nature allows various design structure to be adopted for the overall fault diagnosis process and presents a unique approach to the use of neural networks. This study have contributed by verifying the concept of: a) Using simple circuit status to rapidly reduce the fault diagnosis problem by identifying “fault regions” through the innovative concept of tracing of generators in the network [6] b) Using wavelet transients to “break down” the high speed transient waveforms captured during the initial cycles of the fault, verifying the concept of a “fault signature” of these faults based on the component type and topological location, and finally, c) Using standard pre-trained neural modules, trained to identify specific component such as a transformer or busbar, to be further trained to adapt its diagnosis capabilities to its location based on the electrical connection. This greatly increases its fault diagnosis ability tremendously. This method of training greatly reduces the need to train modules 3 from scratch and reduces the cost and amount of training time as compared to standard neural modules. Hence, developing automatic systems that are able to identify precisely and locate the fault in high voltage networks is far from being trivial, mainly because of the volume and the uncertainty of the information available to the utility operator and most important of all, the stress and urgency of the problem. 1.2 Current Situation in fault diagnosis Rule Based Expert Systems (RBES) is one of the most popular schemes for power systems fault diagnosis. One of the most important requirements for fault diagnosis of power system is adequate response time, especially stringent in real-time environment. As the size and complexity of the knowledge base increases, conventional expert system will be slowed down with the firing of unnecessary rules. Next, a complete description of all connections would increase the number of rules to an extent, that they would not be comprehensible anymore. Heuristic methods would not be complete, as interactions between the protection systems, whether physical or topological, could not be taken into symptom-fault catalogs exhaustively. Hence, speed of such RBES systems and its increasing complexity of the knowledge base have always been a daunting problem that plague such present systems Alternative schemes have been proposed, such as Model-Based Expert Systems (MBES), where systems typical behaviors or models are identified and categorized to accept 4 certain variations in captured results. Lastly, a more advanced hybrid of both expert and modal based systems would be more ideal to capture all scenarios. In case of the model-based diagnosis, the correct solution is not reached by processing known symptoms, as in the heuristic method, but is based on the expected, i.e. correct behavior of the system. The sources of the diagnostic information are discrepancies between the expected and the observed behavior. The required expectations are based on a model of what should happen. This gave us the difficult task of finding the right model for all fault scenarios. Even so, such techniques have been relatively well researched and utilized in major utilities all around the world with tremendous success, as in the case of the Singapore power network which utilizes such a hybrid of expert and modal-based approach to capture all scenarios. The most daunting problem of all is, such state-of the-art diagnostic schemes are still unable to identify multiple fault locations in a network, say a ring configuration network, or in an unusual network. Such inefficiencies have been proven to be expensive and cumbersome, as engineers would have to go down to the faulted region to physically check through all components. 5 Consider this G1 G2 T2 T1 E/F E/F BUp L1 L2 E/F E/F B1 L4 L3 E/F B2 E/F B3 E/F B4 L5 B8 B7 E/F E/F L9 L8 BUp B6 E/F L7 E/F B5 BUp L6 BUp Fig .1.1 A typical ring configuration Alarms: - Backup protection at B7 and B8 - Backup protection at B6 and B7 - Backup protection at B4 and B5 - Backup protection at B2 and B3 - CBs open at B7 and L7 - CBs open at B6 and B7 - CBs open at B4 and B5 - CBs open at L3 - E/F - indications all over the ring to the source Under the present state of the art expert system, the output can only be: Diagnosis: - Fault somewhere between Busbar B7 and L3. 6 In case of Backup protection operation in ring configuration, the diagnosis using the expert system may not be precise because a lot of equipments are fault candidates. If the number of fault candidates is too high, the fault location can only be shown in a global manner (e.g. "Fault somewhere between A and B", or even "Fault somewhere" etc). Such a diagnosis is expensive and disruptive to both the power company and to the end user. 7 1.3 Objectives In this thesis, the primary strategy is to reduce the size of the problem after each stage of the fault diagnosis. This is in line with the concept of ‘divide and conquer’, which will help to alleviate the stringent condition for adequate response time and speed up the entire diagnostic process. By first utilizing a rapid filter followed by a precise identification process using the neural network, it aims to improve the accuracy, efficiency and speed of the fault diagnosis problem. The following list the objectives of this project: ?? The first objective is to design a fault diagnosis system using a typical ring configuration. ?? The second objective is to test the efficiency and feasibility of a rapid filter, called the Intelligent Alarm Filter Processing, to estimate the region at fault, thereby reducing the scale of the fault diagnostic process. ?? The third objective is to test the new concept of using Discrete Wavelet Transform (DWT) values of transient current and voltage of typically less than 7 cycles to identify a profile or signature of fault through such transformation. These signatures are then trained using neural networks for identification of the location of multiple faults. They do not occur at the same time but is impossible to identify in the case of such ring configuration when only the secondary protection trips. ?? Fourthly, to test the effectiveness of a new form of highly flexible ‘target’ neural networks or small neural networks, connected in series or in parallel, trained to identify specific components. 8 ?? Fifthly, to test the effectiveness of using standard modules for each type of component like transformer, busbar and lines as “platforms”. They would then be target trained to adapt to the location of that component, saving computation costs and time. 1.4 Organisation of Report This report is organised into 8 chapters with the first chapter describing the objectives and the principles that this project is based on. It will also describe the current problems faced by existing fault diagnosis techniques towards fault diagnosis, especially towards multiple fault diagnosis. Chapter 2 describes the configuration of the ring network diagram of this study, the approach of the proposed solution and the use of wavelets. Next, chapter 3 will describe the Neural Network and the back-propagation training method. It will also illustrate the decision to adopt the use of neural networks and its extension to use target neural networks. Chapter 4 will describe the use of wavelets, its approach and its suitability towards un-stationary signals. The Alarm Filter processing (IAFP) is described in detail in Chapter 5, on the design, the logic and the tests undertaken to investigate it. It will also include the implementation of the IAFP that is adopted for this project. This will be followed by Chapter 6, which documents the implementation of the network architecture and the design of the first prototype. Chapter 7 will contain the simulation results for the neural design and its performance when implemented with an Expert System such as the IAFP. This is followed by the conclusion at Chapter 8. 9 CHAPTER 2 ALGORITHMIC STRUCTURE OF THE PROPOSED APPROACH To develop an effective diagnostic process to identify and locate the presence of fault, several factors have to be considered. They include a) response speed b) general applicability to all networks c) ability to handle multiple faults d) able to adjust to network configuration changes e) low setup costs f) robustness g) ease of usage Based on the above-mentioned criteria, several solutions are considered. They include Rule –Based Expert Systems (RBES), Modal Based Expert System (RBES) and Artificial Neural Networks. The choice of the technique, however, is dependent on the type of information available. In this thesis, it is proposed to use a combination of Rule- Based Expert System and Artificial Neural Networks. The choice is because of the speed of the Expert System and the ability of the Neural Networks to adapt and learn the complex situations. 10 2.1 A Typical Distribution Ring Network The following network is a typical distribution ring network taken from a sample network. Details of the load level, line characteristics and transformer ratings can be found at the appendix. Fig. 2.1. A sample distribution ring network L13 L14 L17 B12 B13 B14 B18 B17 B16 B15 L20 L2 B1 L16 B11 L21 L1 L15 L19 L3 B2 L18 L4 L5 B3 B4 B6 L6 L12 B10 B9 L10 T1 B8 L9 B7 B5 L8 L7 L11 B30 T2 L23 L24 L25 L32 L33 L34 B21 B19 B26 B27 B28 B20 B25 B24 B23 L30 L22 L29 L28 T3 B31 B29 B32 L27 T4 Legend L = Lines B = Busbar T = Transformer 11 2.2 Generating Fault Data from the Sample Network 2.2.1 Electromagnetic Transients Program (EMTP) Electro-Magnetic Transients Program, is a popular circuit simulation package in power engineering study. It is a universal program system for digital simulation of transient phenomena of electromagnetic as well as electromechanical nature. With this digital program, complex networks and control systems of arbitrary structure can be simulated. It has extensive modelling capabilities and additional important features besides the computation of transients. 2.2.2 Operating Principles ?? Basically, trapezoidal rule of integration is used to solve the differential equations of system components in the time domain. ?? Non-zero initial conditions can be determined either automatically by a steady state, phasor solution or they can be entered by the user for simpler components. ?? Interfacing capability to the program modules TACS (Transient Analysis of Control Systems) and MODELS (a simulation language) enables modelling of control systems and components with non-linear characteristics such as arcs and corona ?? Symmetric or unsymmetrical disturbances are allowed, such as faults, lightning surges, and any kind of switching operations including commutation of valves. In this study, simple line to ground faults are simulated and transient recording during the initial cycles are taken and recorded for neural training. 12 2.2.3 Components ?? Transmission lines and cables with distributed and frequency-dependent parameters ?? Non-linear resistances and inductances, hysteretic inductor, time-varying resistance, TACS/MODELS controlled resistance. ?? Components with non-linearities: transformers including saturation and hysteresis, surge arresters (gapless and with gap), arcs ?? Ordinary switches, time-dependent and voltage-dependent switches, statistical switching. ?? Analytical sources: step, ramp, sinusoidal, exponential surge functions, TACS/MODELS defined sources ?? Rotating machines: 3-phase synchronous machine, universal machine model. ?? User-defined electrical components that include MODELS interaction Summarised as shown below as A COMPONENT TABLE TABLE 1 A Component table Component Type ATP-Element Identification LINEAR BRANCHES - Type 0: uncoupled Lumped series RLC element Type 1, 2, 3 ,…: Mutually Coupled ? - circuit 13 - Type 51, 52, 53,…Mutually coupled RL elements - Type –1, -2, -3, … : Distributed parameter line Models 1. Constant parameter line model (Clark. KC Lee) 2. Special double circuit distributed line 3. SEMIYEN line Model 4. JMARTI line Model 5. NODA Line Model - Saturable Transformer component (multi-winding) 1. TRANSFORMER Single Phase Units 2. TRANSFORMER THREE PHASE with zero sequence coupling 3. IDEAL TRANSFORMER component - BCTRAN supporting routine - KIZILCAY F-DEPENDENT (high order admittance branch) - CASCADED Pt – type 1, 2, 3 element (for steady state solution) - PHASOR BRANCH [Y] –type 51,52,53 element (for steady state solution and Frequency Scan computation) NON-LINEAR - Type 99 : Pseudo – non-linear resistance BRANCHES - Type 98 : Pseudo - non-linear inductance - Type 97 : Staircase time-varying resistance - Type 96: Pseudo – non-linear hysterics inductor 14 - Type 94 : User defined component Via Models - Type 93 : True, non-linear inductance - Type 92 : - 1. Exponential Zone surge arrestor 2. Multi-phase piece-wise linear resistance with flashover SWITCHES - Type 91 : Multi-phase time varying resistance TACS/MODELS controlled resistance - User supplied Fortran non-linear element - Type 0 : standalone switches, 1. time-dependent and 2. voltage-dependent switches, 3. statistical switching. - TACS/MODELS controlled switches SOURCES - EMPIRICAL Sources ($INSERT option) - Analytical sources: 1. Type 11: step function 2. Type 12: ramp function 3. Type 13: Two step linearized surge function 4. Type 14: sinusoidal/Cosine Function/Trapped charge 15 5. Type 15: Exponential surge functions 6. Type 16: Simplified AC/DC converter Model 7. Type 18: Ideal transformer/ungrounded voltage source USER-DEFINED - TACS/MODELS defined sources - Rotating Machines - Type 94 : MODELS controlled electrical branch COMPONENTS 1. Thevenin type Model 2. Iterated type Model 3. Non-transmission Norton Type model 4. Transmission Norton Type Model 2.2.4 Integrated Simulation Modules MODELS in EMTP are a general-purpose description language supported by an extensive set of simulation tools for the representation and study of time-variant systems. ?? The description of each model is enabled using free-format, keyword-driven syntax of local context and that is largely self-documenting. 16 ?? MODELS in ATP allow the description of arbitrary user-defined control and circuit components, providing a simple interface for connecting other programs/models to ATP. ?? As a general-purpose programmable tool, MODELS can be used for processing simulation results either in the frequency domain or in the time domain. TACS is a simulation module for time-domain analysis of control systems. It was originally developed for the simulation of HVDC converter controls. For TACS, a block diagram representation of control systems is used. TACS can be used for the simulation of ?? HVDC converter controls ?? Excitation systems of synchronous machines ?? power electronics and drives ?? electric arcs (circuit breaker and fault arcs). Interface between electrical network and TACS is established by exchange of signals such as node voltage, switch current, switch status, time-varying resistance, and voltage and current sources. The inter-relation of these models is shown in the figure 2.2 below. 17 Fig. 2.2 Inter-relation models between EMTP routines 2.2.5 Supporting Routines These supporting routines allow the simulation of system components like cables, busbars and transformers. ?? LINE CONSTANTS – is a supporting routine for the calculation of electrical parameters of overhead lines and cables lines in a frequency domain like per length impedance and capacitance matrices, ? - equivalent, model data for 18 constant-parameter distributed line (CPDL) branch. This is demonstrated in the coming paragraphs for TRANSMISSION LINE Models in EMTP. LINE CONSTANTS is internally called to generate frequency data for the line models SEMLTENSETUP, JMARTI SETUP and NODA SETUP. ?? CABLE CONSTANTS/ CABLE PARAMETERS are supporting routines to compute electrical parameters of power cables. CABLE PARAMETERS is newer than CABLE CONSTANTS and has additional features like handling of conductors of arbitrary shape, snaking of cable system and distributed shunt admittance model. CABLE CONSTANTS is linked to SEMLYEN and JMARTISETUP whereas CABLE PARAMETERS is called by NODA SETUP to generate frequency dependent electrical parameters. ?? SEMLYEN SETUP is a supporting routine to generate frequency-dependent model for overhead lines and cables. Modal theory is used to represent unbalanced lines in time-domain. Modal propagation step response and surge admittance are approximated by second order rational functions with real poles and zero. ?? JMARTI SETUP generates high order frequency-dependent model for overhead lines and cables. The fitting of modal propagation function and surge impedance is performed by asymptotic approximation of the magnitude by means of a rational function with real poles. JMARTI line model is not suitable to represent cables. 19 ?? BCTRAN is an integrated supporting programs in the ATP-EMTP, that can be used to derive a linear [R], [wL] or [A], [R] matrix representation for a single phase transformer using data of the excitation test and short circuit test at rated frequency. For three-phased transformers, both the shell type (low homopolar reluctance) and the core type (high homopolar reluctance) transformers can be handled by the routine. ?? XFORMER is used to derive a linear representation for single phase, 2- and 3winding transformers by means of RL coupled branches. BCTRAN is preferred over XFORMER ?? SATURA is a conversion routine to derive flux-current saturation curve from either RMS voltage-current characteristic or current incremental inductance characteristics. Flux-current saturation curve is used to model a non-linear inductance, e.g. for transformer Modelling ATPDraw has this feature integrated in the model Saturable 3 phase transformer. ?? ZNO FITTER can be used to drive a non-linear representation (typ-92 branches) for a zinc oxide surge arrestor, starting from manufacturer’s data. ZNO FITTER approximates manufacturer’s data (voltage-current characteristics) by a series of exponential functions of type – T = p{ V/Vref}q 20 ?? DATA BASE MODULE allows the user to modularise network sections. Any module may contain several circuit elements. Some data. Such as node names and numerical data may have fixed values in the module, whereas other data can be treated as parameters that can be passed to the data base module, when the module is connected to the data case via SINCLUDE. ATP-EMTP is used world-wide for switching and lightning surge analysis, insulation coordination and shaft torsion oscillation studies, protective relay modelling, harmonic and power quality studies, HVDC and FACTS modelling. Typical EMTP studies are: ?? Lightning over voltage studies ?? Switching transients and faults ?? Statistical and systematic over voltage studies ?? Very fast transients in GIS and groundings ?? Machine modelling ?? Transient stability, motor start-up ?? Shaft torsion oscillations ?? Transformer and shunt reactor/capacitor switching ?? Ferro resonance ?? Power electronic applications ?? Circuit breaker duty (electric arc), current chopping 21 ?? FACTS devices: STATCOM, SVC, UPFC, TCSC modelling ?? Harmonic analysis, network resonance ?? Protective device testing 2.2.6 Review of solution methods in ATP-EMTP The time domain and frequency domain solution methods in ATP-EMTP will be reviewed briefly. Detailed analysis of numerical modelling of system components and electrical networks are given in the EMTP Theory Book(TB). The review focuses rather to the application features and limitations of the solution method. Time-domain Solution methods The electric network is described in ATP-EMTP using node equations, i.e. node voltages are unknown quantities to be determined. Branch currents are expressed as functions of the node voltages. The solution for each element in time-domain is performed using time step discretization. The value of all system variables are supposed to be known at t- ?t and their value is to be determined at time t. The time step ?t is assumed to be so small that the differential quantities are approximated by difference equations. For example, a simple algebraic relationship is obtained by replacing the differential equation for an inductance. 22 V= L{?i/?t) (1) With a central difference equation that is equivalent to the numerical integration of I using trapezoidal rule for one time step [V(t)+ v(t - ?t)]/2 = L [i(t) – i(t-- ?t )]/2 (2) i(t) = G.v(t) + Ihist (t-- ?t ) (3) G = ?t/(2L).G is the equivalent conductance that remains constant, when the time step ?t of the computation constant Ihist (t-- ?t ) is the history term composed of known quantities from the preceding time step having the unit of ampere. A similar formulation can be written for capacitor and resistor. For multi-phase coupled elements this basic formulation still holds. The equations of multi-phase coupled elements are incorporated into the nodal admittance matrix of the electrical network. For any type of network with n nodes, a system of n such equations can be formed. [G][v(t)] = [i(t)] – [Ihist] with (4) [G] : n x n (symmetric) nodal conductance matrix [v(t)] : vector of n node voltages [i(t)] : vector of n current sources, and [Ihist] : vector of n known “history” terms 23 Normally some nodes have known voltages source either because voltage sources are connected to them, or because the node is grounded. In this case Eq. 4 is partitioned into a set of A of nodes with unknown voltages, and a set B of nodes with known voltages. The unknown voltages are then found by solving [GAA][vA(t)] = [iA(t)] – [Ihist] – [GAB][VB(t)] for VA(t). The actual computation in the EMTP proceeds as follows: Matrices [GAA] and [GAB] are built, and [GAA] is triangularized with ordered elimination and exploitation of sparsity. In each time step, the vector on the right hand side of Eq. 5 is updated from known history terms, and known current and voltage sources. Then the system of linear equations is solved for [vA,t], using the information contained in the triangularized conductance matrix. In this “repeat solution” process, the symmetry of the matrix is exploited in the sense that the same triangularized matrix used for download operation is also used in the back substitution. Prior to the next time step, the history terms in the next time step, the history terms included in [Ihist] are then updated for use in the following time step. The transient simulation can be started from 1) zero initial conditions. 2) A.c steady state initial conditions at a given frequency (one source) or superimposed by means of more sources with different frequencies. 24 The actual details on short-circuiting of a capacitor, interruptions of current through an inductor and the treatment of Nonlinear and Time varying elements can be found in The EMTP Theory Book [23]. Here is a brief summary: The most common type of non-linear elements are nonlinear inductances for the representation of transformers and shunt reactor saturation, nonlinear resistances for the representation of surge arrestors, and a time varying resistances for the representation of an electric arc. For each time step, the value of the equivalent arc resistance is determined by solving a differential equation of arc conductance. Nonlinear effects in synchronous machines are handles in the machine equations directly. Usually, the network contains only a few nonlinear elements. It is therefore sensible to modify the well-proven linear methods more or less to accommodate nonlinear elements, rather than to use less efficient nonlinear solutions for the entire network. Two methods have been used to model nonlinear elements in ATP-EMTP. 1. Compensation method [23] a. Type 93: True, non-linear inductance b. Type 92: - i. Exponential Zone surge arrestor ii. Multi-phase, piecewise linear resistance with flashover c. Type 91: Multi phase – time varying resistance 25 d. Type 91: TACS/MODELS controlled resistance (arc modelling) 2. Pseudo-non-linear representation a. Type 99: Pseudo-non-linear resistance b. Type 98: Pseudo-non-linear inductance c. Type 97: staircase time varying resistance d. Type 96: Pseudo-non-linear hysteretic inductor 2.2.7 Simulation of Transient Behaviors using EMTP Using EMTP, considerations must be taken such that the simulation results are as good as the model that is implemented. These include: – Frequency range of the behaviour being simulated Frequency range of the behaviour being simulated – Available models for each component in the simulation package being used. – Models may not be, as matured and certain intelligence in the modelling has to be considered The following document certain steps in the problem taken in the modelling of the network. a) Constructing the overall system model b) Obtaining parameters for components c) Benchmarking the component models. Benchmarking the component models. 26 d) Benchmarking the overall system e) Running the simulations and getting results. 2.2.8 Transmission Line Models in EMTP • The model chosen depends on line length and the highest frequency to be simulated. • For “short” or “medium” lines, a simple For “short” or “medium” lines, a simple lumped coupled- lumped coupled-B or several cascaded in or several cascaded in series may be sufficient. • Distributed parameter lines are typically used except for some of the shortest line sections. • Various distributed line models exist, each using different representations and characteristic impedance and frequency. Basic Features of Distributed Parameter Line Models Parameter Based on the typical traveling wave equations - ?V -------- = Zi ?X - ?I -------- = Yv ?X v and i are the vectors of node voltage and line currents at distance x from receiving end of line. Model Derivation Details given in references [14,16,17,18,19] Details given in references [2,4,5,6,7] Basic approach: Basic approach: - Derive model in frequency domain. 27 - Use Modal Transformations to decouple phases. - Use convolution/deconvolution methods to convert frequency domain solution to time-domain “equivalent” that can be implemented via numerical integration methods. - Problematic realizations Problems: - Derived model is only valid for the frequency at which the Modal Transformation was done. - Frequency fitting techniques can be used to improve results [19]. - Newer models are based in the phase domain and avoid the Modal Transformation [20]. Transmission Line Data - A Dilemma Existing transmission line data may exist only in a format useful for load flow, short circuit, and stability studies. Short-circuit data: positive and zero sequence per-phase series impedances. For doublecircuit lines, you will also have the “mutual impedance” -- the zero-sequence coupling between circuits. Total positive sequence line-charging MVA may be known. I For load flow and stability studies, you will have short-circuit data as above, plus positive and negative sequence capacitive A Data Base of Physical Design Parameters must be developed: a) (x, y) coordinates of conductors and shield wires. b) Bundle spacing, orientations. 28 c) Phase (A,B,C) and circuit (1,2,3) designation of each conductor d) Phase “rotation” or swapping at each transposition e) Physical dimensions of each conductor (both Physical dimensions of each conductor (both inner and outer radii for bus bar and stranded inner and outer radii for bus bar and stranded conductor). f) Earth resistivity of the ground return path g) Other information such as segmented grounds, transpositions. Transmission Lines - One Example 345-kV Single-Circuit Physical Input Parameters. The following Fig. 2.3 illustrates the typical input format for line parameters. Fig 2.3. Typical input format for line parameters Typical set of input, representative of most so-called “LINE CONSTANTS” programs or supporting routines. Comments on Parameters • Conductor Numbers 1,2,3 ==> phase A,B,C • Conductors Numbered 0 ==> shield wire 29 • Inner Radius: Radius of steel stranded core or Inner or radius of inner tube diameter of bus bar • DC Resistance: usually choose to be at 50°C or higher, not at 25 °C • Horizontal (x-axis) value: relative horizontal position of each conductor. Absolute value is not important • typical height at tower and height at midspan are used to determine average height above terrain assuming a catenary sag. • Bundled conductors are usually handled as a group: number in bundle, separation, orientation of group Choice of Model to Implement • Accuracy of model for given range of frequencies is key concern. • several “standard” choices, available in the original EMTP program are discussed here models may be available in various simulation models packages. – Lumped parameters coupled PI. – Bergeron (distributed parameter, constant Z (distributed parameter, constant ZC), also referred to as “Constant Parameter” which is built on the paper by Snelson [16] and developed by Meyer and Dommel [17]. – J-Marti Model (distributed parameter, constant Z c) • with physical parameters entered, various models for a given line section can be implemented and benchmarked until satisfied. 30 2.2.9 Transformer Modeling in EMTP What is needed in Transformer Models? The amount of detail used in transformer modeling depends on what sort of response that we are trying to model. For a power flow or stability program, a per unit (P.U.) representation is adequate, with tap changing or phase shifting ability included in where appropriate. More information about the connection and grounding of the transformer is needed for fault studies. Transformers with more than 2 winding also need to be considered. The same sort of models can be used for low frequency transients, although the actual inductance values need to be used rather than per unit quantities. The EMTP representation uses the actual windings, so the turns or voltage transformation ratios for the actual winding should be used. So for example a 230:13.8 kV-Y ? ?would have ratios on the single-phase windings of _ 230:13.8/? 3 kV. The transformer impedances can be included on each winding, or in some cases referred to one side of the transformer. A single-phase transformer in Fig. 2.4 would be modeled as shown below: R+ j X R+ j X R Fig. 2.4 A typical Single Phase Transformer 31 The core loss resistance represents the total core losses in the transformer, and in some cases the date may be given as a loss component that must be converted into a resistance. The magnetizing reactance is subject to saturation, and that must be considered as well. There are a few options for modeling saturation in EMTP; these are the Type 96 and Type 98 non-linear inductor models. As mentioned above, the transformer connection is important in modeling a transformer for transients. This includes whether the transformer is connected in WYE, delta, in an autotransformer, zigzag transformer, has three (or more windings), grounding and so on. Node names are chosen appropriately to connect the windings. The simpler transformer models represent a three-phase transformer as three single-phase transformers, each with their own core in the case of the saturable transformer. However, there are models for multiphase cores as well. The saturable transformer component can model transformers with more than 2 windings, as can the single core models described below. Stray capacitances, winding to winding, turn to turn, turn to ground, etc. become more important at higher frequencies. The data for these capacitances is rather difficult to come by in some cases. In addition, the built-in transformer models in most versions of EMTP are unable to represent these. EMTP96 from EPRI/DCG may have a model. Otherwise, a customized model must be developed on their own. UI: EE524 Transients in Power Systems Session 33; Page 2/8 Spring 2001 32 2.2.10 Available EMTP Models 2.2.10.1 K Type 18 Ideal Transformer/Source Vs(t) - + 1: n J Floating source M L The type 18 devices combine an ideal transformer with a floating source. The model is as shown below: C ..........................Typical .Source data ...................................... C Bus-->[...]... simulation module for time-domain analysis of control systems It was originally developed for the simulation of HVDC converter controls For TACS, a block diagram representation of control systems is used TACS can be used for the simulation of ?? HVDC converter controls ?? Excitation systems of synchronous machines ?? power electronics and drives ?? electric arcs (circuit breaker and fault arcs) Interface between... (Transient Analysis of Control Systems) and MODELS (a simulation language) enables modelling of control systems and components with non-linear characteristics such as arcs and corona ?? Symmetric or unsymmetrical disturbances are allowed, such as faults, lightning surges, and any kind of switching operations including commutation of valves In this study, simple line to ground faults are simulated and transient... world-wide for switching and lightning surge analysis, insulation coordination and shaft torsion oscillation studies, protective relay modelling, harmonic and power quality studies, HVDC and FACTS modelling Typical EMTP studies are: ?? Lightning over voltage studies ?? Switching transients and faults ?? Statistical and systematic over voltage studies ?? Very fast transients in GIS and groundings ?? Machine... controlled electrical branch COMPONENTS 1 Thevenin type Model 2 Iterated type Model 3 Non-transmission Norton Type model 4 Transmission Norton Type Model 2.2.4 Integrated Simulation Modules MODELS in EMTP are a general-purpose description language supported by an extensive set of simulation tools for the representation and study of time-variant systems ?? The description of each model is enabled using. .. swapping at each transposition e) Physical dimensions of each conductor (both Physical dimensions of each conductor (both inner and outer radii for bus bar and stranded inner and outer radii for bus bar and stranded conductor) f) Earth resistivity of the ground return path g) Other information such as segmented grounds, transpositions Transmission Lines - One Example 345-kV Single-Circuit Physical Input... actual computation in the EMTP proceeds as follows: Matrices [GAA] and [GAB] are built, and [GAA] is triangularized with ordered elimination and exploitation of sparsity In each time step, the vector on the right hand side of Eq 5 is updated from known history terms, and known current and voltage sources Then the system of linear equations is solved for [vA,t], using the information contained in the... component that must be converted into a resistance The magnetizing reactance is subject to saturation, and that must be considered as well There are a few options for modeling saturation in EMTP; these are the Type 96 and Type 98 non-linear inductor models As mentioned above, the transformer connection is important in modeling a transformer for transients This includes whether the transformer is connected... and cables Modal theory is used to represent unbalanced lines in time-domain Modal propagation step response and surge admittance are approximated by second order rational functions with real poles and zero ?? JMARTI SETUP generates high order frequency-dependent model for overhead lines and cables The fitting of modal propagation function and surge impedance is performed by asymptotic approximation... cycles are taken and recorded for neural training 12 2.2.3 Components ?? Transmission lines and cables with distributed and frequency-dependent parameters ?? Non-linear resistances and inductances, hysteretic inductor, time-varying resistance, TACS/MODELS controlled resistance ?? Components with non-linearities: transformers including saturation and hysteresis, surge arresters (gapless and with gap),... torsion oscillations ?? Transformer and shunt reactor/capacitor switching ?? Ferro resonance ?? Power electronic applications ?? Circuit breaker duty (electric arc), current chopping 21 ?? FACTS devices: STATCOM, SVC, UPFC, TCSC modelling ?? Harmonic analysis, network resonance ?? Protective device testing 2.2.6 Review of solution methods in ATP-EMTP The time domain and frequency domain solution methods ... (Transient Analysis of Control Systems) and MODELS (a simulation language) enables modelling of control systems and components with non-linear characteristics such as arcs and corona ?? Symmetric... typical ring configuration Alarms: - Backup protection at B7 and B8 - Backup protection at B6 and B7 - Backup protection at B4 and B5 - Backup protection at B2 and B3 - CBs open at B7 and L7 - CBs... initial cycles of the fault, verifying the concept of a fault signature” of these faults based on the component type and topological location, and finally, c) Using standard pre-trained neural