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    • 专利摘要:
    SYSTEM AND METHOD FOR DETECTING INCIPIENT FAILURES OF GENERATOR. This is a method, system and computer software for detecting an incipient failure of a generator in an electrical system, including the steps of securing one or more generator setpoint parameters for use as a power line reference. base; measure one or more values of the generator's operational parameter; use the one or more operating parameter values to solve an estimated present value of one or more of the generator's current performance parameters using the particle cloud optimization technique; and determining whether the estimated present values of one or more of the generator's current performance parameters are outside an acceptable limit.
    公开号:BR112014010913B1
    申请号:R112014010913-3
    申请日:2012-11-07
    公开日:2021-02-02
    发明作者:Kiyong Kim
    申请人:Basler Electric Company；
    IPC主号:
    专利说明:

    TECHNICAL FIELD
    [0001] The present disclosure relates to alternating current power generators and, more specifically, to a system and method for managing and maintaining the power generator. BACKGROUND
    [0002] A serious problem in the generation of electrical power in conjunction with the ever-increasing power supply grids in size of utility systems is that the generator falls out of time and expensive at one of the generators within the supply grid power.
    [0003] An electric power generator receives rotational force from a driving machine that rotates a coil of wire in relation to a magnetic field or vice versa. In electric generators, this magnetic field is generated with the use of electromagnets known as field coils. An electrical current in these field coils provides the magnetic field needed to induce an electrical current in the main generator coil to produce the power generated for delivery to the power supply grid.
    [0004] Reactive or voltage / amp / reactive (VAR) generators need to develop voltage and reactive planning, operating practices and procedures to ensure that they have sufficient reactive resources, voltages and reactive margins to supply energy from AC to the power supply grid. As experienced in the past with power supply grids, an unplanned generator outage can have a negative effect on the entire power supply grid and can result in successive blackouts. While standards have been developed by industry organizations such as the North American Electric Reliability Corporation (NERC) for voltage, reactive control and planning, such standards only provide planning, design and operation for a generator. within the power supply grid and does not address preventive maintenance or the prediction of potential generator failures, such as, for example, short circuits in the field winding. As such, the inventor has identified a need for a system and method that can detect incipient faults or generator failures to enable an operator to plan and implement maintenance on the generator to prevent unplanned generator outages. BRIEF DESCRIPTION OF THE INVENTION
    [0005] The present invention provides a method, system and computer software for detecting an incipient failure of a generator in an electrical system that includes the steps of determining one or more generator generator set parameters for use as a reference baseline; measure one or more values of the generator's operational parameter; use the one or more operational parameter values to solve an estimated present value of the one or more of the generator's current performance parameters using the particle cloud optimization technique; and determining whether the estimated present values of one or more of the generator's current performance parameters are outside an acceptable limit. BRIEF DESCRIPTION OF THE DRAWINGS
    [0006] Figure 1 is a diagram of a system for estimating generator parameter values according to an exemplary modality.
    [0007] Figure 2 is a flowchart of a system for estimating generator parameter values according to an exemplary modality.
    [0008] Figure 3 is a diagram of a system for estimating generator parameter values according to an exemplary modality.
    [0009] Figure 4 is a flowchart of a system for estimating generator parameter values according to an exemplary modality.
    [0010] Figures 5 to 8 are screen shots of a system for estimating generator parameter values according to an exemplary mode.
    [0011] Figure 9 is a phasor diagram of a generator according to an exemplary modality.
    [0012] Figure 10 is a diagram of a system for estimating generator parameter values according to an exemplary modality. DETAILED DESCRIPTION OF THE PREFERENTIAL MODE
    [0013] The following description is merely exemplary in nature and is not intended to limit the present disclosure or the applications and uses of the disclosure.
    [0014] The present system and method are applicable to AC power generators within an AC power supply grid.
    [0015] A typical model for such an AC power generator and associated electrical system components are illustrated in Figure 1. As shown, generator 12 receives a rotational force input from a driving machine 14 that can, for example, be driven by water steam turbine, powered by gas turbine, powered by hydro or powered by diesel. The generator 12 receives a field voltage from an exciter 16 that supplies the field coils in the generator 12 at a variable level. The amount of field voltage supplied by exciter 16 to generator 12 is determined by an automatic voltage regulator (AVR) 18. AVR 18 determines the appropriate amount of field voltage to deliver to generator 12 based on the operational needs of the electrical system. An electrical system stabilizer (PSS) 20 can interwork with the AVR 18 to stabilize the power generated by the generator 12. The AVR 18 and / or the PSS 20 monitor a power supply grid 22 and the voltage of the terminals and current at the outputs generator 12 to ensure that generator 12 is operating as desired. The power supply grid 22 is modeled representing transformers 24, transmission lines 26, as well as a power factor load 28 and large motor starting loads 30.
    [0016] As shown in Figure 2, the following describes an exemplary embodiment of an incipient failure generator detection method. The steps in Figure 2 are described below and with reference to the other Figures. In the description of the various flowcharts, the functional explanation of a step is marked with numerals in angle brackets <nnn>, Step 1: Receive and Store the <200> Generator Inputs Benchmark:
    [0017] The first step <200> of the example process is to receive and store the generator setpoint values. The generator setpoint values are either as calculated or measured by the manufacturer at the time of manufacture of the generator or as otherwise determined or measured.
    [0018] These benchmark values provide a baseline and reference input for assessing generator performance and include design parameters and performance parameters and are referred to in this document as generator design benchmarks. Some parameters are fixed over the life of the generator, such as, for example, the number of windings and poles or may vary during the operation of the generator due to the speed, temperature or field voltage of the generator operation or other factors such as as, for example, the resistance of the armature winding Ra, which varies with temperature. Other manufacturer parameters cannot be directly measured in real time while the generator is running.
    [0019] Examples of generator reference parameters for which values can be measured or calculated for a specific generator are: Ra = resistance of the armature winding. Rfd = field winding resistance Xl = leakage reactance Xd = steady-state direct axis synchronous reactance Xq = steady-state square synchronous reactance X'd = direct-axis synchronous reactance X'q = dynamic quadrature synchronous reactance T 'do = generator open circuit direct axis time constant T'qo = generator open circuit quadrature time constant
    [0020] The step of receiving and storing generator setpoint entries can include a user interface such as, for example, a graphical user interface (GUI) to enter such values and differences within the system. Step 2: Measurement of the Operational Parameter Values:
    [0021] The next step <202> is the measurement of the operational parameter values during the operation of the generator. Operational parameter values comprise directly measurable steady state and generator performance or dynamic state reference values that include, but are not limited to, one or more of the terminal voltages per phase, the terminal currents per phase, the power total P and the internal rotor angle. As known in the art, these measurements can be sampled and recorded over time.
    [0022] In one embodiment, the operational parameters sampled in a set of operating parameters can include one or more of: Efd = field voltage Ifd = field current Et = terminal voltage It = terminal current Φ = the angle of load δi = the energy angle (internal rotor angle) Vgen = generator voltage Vr = reference voltage P = Actual generator energy Q = Reactive generator energy
    [0023] If an energy angle δi cannot be measured, for example, where an energy angle measuring device is not available, the energy angle δi can be calculated as noted below.
    [0024] These operating parameter values are provided during system tests or on a periodic operating basis to determine the generator's operating parameter values. In addition, the system and method as described in this document can be continuous during the operation of the generator in a monitoring mode for real-time measurement of operational parameters. Step 3: Estimating Generator Performance Parameters:
    [0025] While the measurable performance values are received above, other aspects of the generator's performance parameters are not directly measurable while the generator is in operation, but if known it would provide understanding regarding the probability of incipient failure of that generator. To estimate the values of the performance parameters, this description results in the application of one or more generator models. The system uses a generator model selected by the user and estimates these parameter values within the generator model selected using a system and the parameter estimation method described below. The parameter estimation system includes a user interface such as a graphical user interface (GUI) to provide a user with the ability to initialize the system and control an online parameter estimation process in real time for a given generator. The parameter estimation system and method as will be described in the present invention uses the sampled operating parameter values measured from field and stator voltages and currents for both dynamic and steady state operating modes to estimate generator performance parameters. An exemplary set of performance parameters that can be estimated is as follows: Xd = steady-state direct-axis synchronous reactance Xq = steady-state quadrature synchronous reactance X'd = direct-axis synchronous reactance X'q = synchronous reactance dynamic quadrature time T'do = generator open circuit direct axis time constant T'qo = generator open circuit quadrature time constant As used in the present invention, the single apostrophe indicates the dynamic parameter compared to the parameter steady state.
    [0026] This exemplary set of estimated performance parameters will hereinafter be referred to as the estimated performance parameters. As will be described, each of these will have their estimated values and these values will be compared to a steady state and reference variance from Step 1 above.
    [0027] It should be understood for those skilled in the art that the identified set of estimated performance parameters is just an exemplary set and less or additional generator parameters can be included within the present system and method and still be within the scope of the present disclosure . The PSO Estimation Strategy
    [0028] The incipient generator failure detection system and method uses particle swarm optimization (PSO) to generate the values of the generator performance parameters in real time based on the current measured operational parameter values and certain values of reference parameter. While the estimated parameter values derived from PSO are based on real-time measurements, the PSO method estimates the parameter values to determine when the value is outside acceptable parameters.
    [0029] For power generation systems, the data available for synchronized generators are the stator phase currents and voltages at the machine terminals and the field voltage and current. Thus, these parameters are used to formulate the parameter estimation problem.
    [0030] The generator field voltage, voltages and currents of the three-phase generator terminal are continuously monitored. If the generator is operating in the steady state, the synchronized generator reactances (Xd and Xq) are estimated based on the steady state condition.
    [0031] When an event causes a considerable change in the generator the field voltage is detected, its dynamic responses are recorded for five seconds. The recorded responses are used to estimate the dynamic parameters of the generator (X 'd, X'q, T'do, T'qo) as shown in Figure 3.
    [0032] The recorded generator terminal field voltages and currents are applied to the time domain simulation to calculate the generator voltages. For the generator model for simulation, the derivative of the geometric axis q flow connections is ignored. Thus, the two-axis generator model represented in the equations below is used which includes the fundamental attributes of voltage responses, but assumes that all stator / network transients have been eliminated. This assumption is correct since the rate of change of the terminal voltage and current of the machine is negligible in the face of small disturbance conditions.

    [0033] The results of the simulation obtained with the use of this generator model are compared with the recorded data. If the results do not match, the generator parameters will be adjusted using the PSO technique to provide the best match. The technique is inspired by the social behavior of flocks of birds or shoals of fish. In PSO, the potential particles (solutions) fly through the problem space followed by the optimal current particles. Each particle maintains information about its coordinates in the problem space and communicates the best solution found for the other particles.
    [0034] This communication allows an intelligent decision in a next attempt to find the best possible solution (a set of generator parameters).
    [0035] The PSO is initialized with a group of five particles and then searches through the problem space for the ideal followed by the optimal particles found so far. The problem solving space is defined as the generator parameters (Xd, Xq, X'd, X'q, T'do, T'qo). With the present particle and the generator field voltage recorded, the model response (yk) is calculated to k = 1, ..., N. The calculated response is compared to the actual system response. Leaving a sampled value of the real system response in stage k to be zk. The suitability function for choosing the best particle is the sum of the square of the differences between zk and yk, k = 1, ..., N as follows:

    [0036] After finding the two best values, the particle updates its speed and its positions with the following equations:

    [0037] While vk is the particle speed, xk is the current particle (solution), xkself and xkglobal are defined as the best value for a particle and the best value among all particles, α is a weight of inertia, rand1 and rand2 are random numbers between 0 and 1. β1 and β2 are learning factors. In this role, the PSO technique described above is modified to achieve a better search for the present problem, as described below. Estimation of Generator Parameters
    [0038] The machine model in the previous section is the standard geq axis model with a damper winding on each geometry axis. In this way, the voltages and currents of the q-axis generator are calculated using the equations below using terminal voltages and sampled three-phase generator correct.

    where δi, Et, It, P and Q are generator rotor internal angle (energy angle), terminal voltage, current, active power and reactive power, respectively. See Figure 9.
    [0039] The necessary measurements for the estimation process are AC voltages and three-phase currents, field voltage and rotor angle. The energy angle is assumed to be obtained from an energy angle measuring device, although an estimated value can be calculated as well, as described below. Power Angle, δi
    [0040] The steady state value of the energy angle, δi, is calculated using the following relationship:

    [0041] Using the known values of Et, It and Φ and the values provided by the manufacturer of Ra, Xq, the energy angle is computed and compared against the measured steady state value. A consistent shift reported between the calculated and measured value of δi is compensated by calibrating it to zero with measured zero active power. Steady state parameters (Xd and Xq)
    [0042] In the steady-state operating condition, the synchronized reactances of generator Xd and Xq) are estimated based on the following equations:
    where KSd is the generator saturation coefficient in the steady state condition. Saturation Coefficient, KSd
    [0043] The parameters of a synchronized machine varies depending on the different loading conditions due to the fact that changes in the internal temperature of the machine, magnetic saturation, aging and coupling between the machine and external systems.
    [0044] Several assumptions are made to represent saturation in temporary stability studies since a rigorous treatment of synchronized machine performance that includes saturation is a futile exercise. A practical method for dealing with the effects of saturation based on semi-heuristic reasoning and carefully chosen approaches are given in [9]
    [0045] The saturation effect is characterized by the saturation functions. This variation causes the field voltage to change. In order to manage the effects of saturation based on the simplicity of the estimation method, the field voltage is multiplied by the saturation coefficient.
    [0046] Based on the terminal voltages and currents of three bases, Pt and Qt are calculated and can be found
    where Ra and Xl are the generator stator resistance and leakage reactance, respectively.
    [0047] Asat, Bsat and Φ ti are generator saturation coefficients. Dynamic State Parameters (X'd, X'q, T'do, T'qo)
    [0048] Dynamic parameters are identified using the PSO technique by comparing the actual measured voltages with the calculated generator voltages. A time domain simulation is performed to calculate the generator voltages when the recorded generator field voltages are applied.
    [0049] The generator parameters for the simulation model are adjusted by the PSO technique to provide the best match. The set of generator parameters are expressed as a particle position. The generator model with each particle position is used to calculate the terminal voltages. For simulating the generator response for the applied field voltage with measured generator currents, the Euler integration method is used to solve ordinary differential equations of dynamic models. Euler's method is used only for the sake of simplicity since the propagation error is negligible with an integration step size of 1 msec.
    where Δt is the size of the integration step and KSd is the generator saturation coefficient calculated in a pre-triggered condition. The value given by the manufacturer or measured is used for the stator resistance Ra. The change in stator resistance for a specified operating temperature can also be calculated according to the prior art.
    [0050] The initial values for the dynamic equations, E'd (0) and E'q (0), are calculated based on the values sampled in pre-trigger conditions using the following equations:

    [0051] The geometric axis values of simulated generator q are compared to the measured values ed_m (k) and eq_m (k), using the suitability function

    [0052] After finding the two best values (best global and best self), the particle updates its speed and its positions with the following equations:
    where vk is the particle speed, xk is the current particle (solution), x xkself and xkglobal are defined as the best value for a particle and the best value among all particles. The weight of inertia, α, is 0.9 and the learning factors, β1 and β2, are 0.1. No random number is multiplied in the above function where the values of the simulated generator's geometric axis q are compared with the values measured using the suitability function. Instead, a new set of particles are generated after 50 iterations as follows: X = rand () Xnoainal (26)
    [0053] The search surface limit for each particle is fixed with the following rules:

    [0054] Referring to Figure 4, the computational procedure of the proposed PSO technique is summarized as follows: Step 0 <701): Initialize the iteration indications, NOI = 0, J = 0 Step 1 <702>: Initialize each particle position using equation (26). Step 2 <704>: Calculate the initial values for the dynamic equations, E'd (0) and E'q (0), for the simulation models using equations (21) and (22) Step 3 <706 >: Calculate model responses with a selected particle position ed (k) and eq (k), k = 1, ..., N, applying the measured field stresses for equations (17) to (20 ). Step 4 <708>: Calculate the adequacy function, equation (23), to check the best particle based on the model responses obtained in step 3 and the recorded responses. If it is better, update the best particle. Step 5 <710>: Until all particles are calculated in step <710>, increment a particle counter <711> and repeat steps <704>, <706> and <708> for each particle. Step 6 <712>: Update the new particle position and velocity using equations (24) and (26). Step 7 <714>: Then in decision block <714>, determine if the maximum number of iterations has been reached. If the maximum number of iterations was not reached in step <716>, increment the iteration counters (NOI and J) and go to step <717>. If reached, the additional estimation of the parameter value is interrupted. Step 8 <717> If index J is 50, go to step <702>. If not, go to step <704>. At the end of the iterative process, the best global value will contain the estimate closest to the parameter value. Step 4 <206>: Compare the Estimated Values to the Received and Stored Reference Values and Variances
    [0055] With reference again to Figure 2, after estimating the performance parameter of the steady state or dynamic state generator, the estimated parameters are compared to the received and stored reference values and the variances of step 1 <200>. Step 5 <208>: Determine whether to set an alarm
    [0056] If in step 4 <206> it is determined that a dynamic or stable state performance parameter is beyond an expected value for a certain amount or beyond a given variance from the expected value, an alarm is displayed (step < 210>) for an operator that a generator performance value is outside the expected value or range. If all parameters are within the expected range, the generator parameter comparison and the estimation process are immediately redisparated or redisparated at a later time based on predefined conditions. Implementation of the Process in a Computing Environment
    [0057] The present invention can be implemented in conjunction with a real generator or in a real-time simulation program to simulate a power generation system in order to test the ability of the present system to successfully estimate the generation parameters. With the utmost preference, the present invention is implemented in an application program based on MICROSOFT WINDOWS that operates on a computer system capable of operating the WINDOWS environment with the present invention that has its own graphical user interface (GUI).
    [0058] To test, a real-time simulation program to simulate a rudimentary power generation system connected to a large electrical system with balanced conditions is provided and is depicted within the application (Figure 5). As seen in Figure 5, the electrical system model in Figure 1 is displayed within the interface and the elements of the model can be selected to define the characteristics of the element within the model through the user interface (for example, the parameter window of generator in Figure 6).
    [0059] The process of measuring the responses of the machine is extracted from the simulation program in real time. By measuring the amounts of generator voltage, current, power and internal rotor angle, the geometric axis voltages and currents deq of the terminal, ed, eq, id and iq can be obtained using equations (7) to ( 10). The noise and trend errors are added to the measured quantities to verify the estimation performance of the proposed method.
    [0060] While the simulation is running, the system model parameters are allowed to change in real time, either automatically through a random script or a stepwise script or through user inputs that manually change one of the quantities or characteristics of the system measured through an interface such as the one in Figure 6 (which shows changes in the generator parameters). Similarly, the interface in Figure 5 can be used to change system elements, for example, to model a power factor load change within the system. In one example, a generator model with two damper windings is implemented in the real-time simulation program through the interface in Figure 6. The generator parameter dialog window in Figure 6 will then appear by clicking on the generator block Figure 5. The GUI application is also designed to control the opening / closing of circuit breaker status by clicking the mouse in the appropriate position in Figure 5. Thus, AVR performance is evaluated for various generators and system configurations as load / reject application, engine start-up problems and other electrical system occurrences.
    [0061] In a preferred mode, a monitoring screen was designed for six system states, Vgen, 'Efd, Ifd, Vr, Pgen and Qgen. As shown in Figure 7, the six system states are selected for the monitor system responses and can be stopped for analysis to press the command buttons to initialize the monitor and to stop the monitor within Figure 7. The graphs showing the parameters over time are displayed and the characteristics of the graph can be controlled by selecting the graph number, determining the parameter that will be shown in that graph number, selecting the minimum and maximum units for the Y geometric axis and manipulating the scale for the time scale, all within the screen of Figure 7. In this way, the responses of systems due to a disturbance can be easily analyzed. Figure 7 shows the monitoring screen with the example of a load and rejection application when the generator breaker and the load breakers are closed and opened.
    [0062] When a trip mode is selected, the generator field voltage is continuously monitored for any significant changes to estimate the generator parameters. Online estimation is disabled if the generator power is less than 10%. For normal operation without any significant change in the system operation, the stable state parameters (Xd and Xq) are estimated. In the preferred mode, if a sudden change in the field voltage is detected, the program collects the dynamic responses of the generator, the field voltage, the rotor position, the voltages and currents for 5 seconds. Pre-triggered values are also collected to calculate the initial conditions for the estimation. These sampled values are used to estimate the generator parameters. If any significant changes in the generator parameters are detected, their results are announced to the operator. Figure 8 shows the typical real-time estimation profiles when a system disturbance is detected.
    [0063] The parameter estimation steps are implemented within the program of the present modality as shown in Figure 8. The interface of Figure 8 shows parameter estimation online for a given synchronized machine based on measurements of field voltages and currents and stator. It was designed to select the estimation modes and monitor the estimated values of six generator parameters in real time. Three modes can be selected from this screen, the step response mode, event trigger mode and test mode to test the identification of the generator parameters in response to the simulated system occurrences. The step response mode is used to estimate the generator parameters by a forced step change at the generator voltage set point. If the generator circuit breaker is open, the generator open circuit time constant (T'do) is estimated. If it is connected to the system, the six parameters, (Xd, Xq, X'd, X'q, T'do, T'qo), are estimated using the proposed method. The test mode is selected if more estimation iterations are required. That is, parameter estimation is performed based on previously recorded values.
    [0064] In addition to graphing the estimated parameter values over time, the interface in Figure 8 (similar to the interface in Figure 7) allows a user to change the x scale and the y scale of the parameter graphics, display the current simulation state, allow manipulation of estimation method settings, such as voltage step (%), change in output (%), amount of sampling time (seconds) of samples taken and tolerance (%) . In addition, the present estimated time parameter values are displayed adjacent to the registered (nominal) generator parameter values and visual indicators of parameters that are beyond acceptable limits are provided. Estimated parameters are continuously displayed and compared with given generator parameters to detect incipient faults. If a parameter is outside the permitted limit, the value is displayed in red on the estimation parameter screen as shown in Figure 8 in 800. Computer Operation Environment
    [0065] With reference to Figure 10, an operating environment for an illustrated embodiment of a system and / or a method for detecting an incipient failure in a generator as described in this document is a computer system 1000 with a computer 1002 comprising at least one high-speed central processing unit (CPU) 1004, together with a memory system 1006 interconnected with at least one bus structure 1008, an input device 1010 and an output device 1012. These elements are interconnected via of at least one bus structure 1008.
    [0066] As addressed above, the input and output devices may include a communication interface that includes a graphical user interface. Any or all of the computer components of the network interface and communication systems and methods can be any computing device that includes, but is not limited to, a lap top computer, PDA, cell phone / mobile phone, as well as potentially a dedicated device.
    [0067] The software can be deployed as any "app" in it and still be within the scope of this disclosure.
    [0068] The illustrated CPU 1004 for a system to detect an incipient failure of a generator is of familiar design and includes a 1014 arithmetic logic unit (ALU) for performing computations, a collection of 1016 registers for temporary storage of data and instructions and a 1018 control unit to control the computer from the 1000 computer system. Any of a variety of processors, which includes at least those from Digital Equipment, Sun, MIPS, Motorola, NEC, Intel, Cyrix, AMD, HP and Nexgen, it is equally preferred, but not limited to them, for the 1004 CPU. This illustrated modality operates on an operating system designed to be portable for any of these processing platforms.
    [0069] The memory system 1006 generally includes high-speed main memory 1020 in the form of a medium such as, for example, random access memory (RAM) and read-only memory (ROM) semiconductor devices that are typical on a non-temporary, recordable computer medium. The present disclosure is not limited thereto and may also include secondary storage 1022 in the form of long-term storage media such as, for example, floppy disks, hard drives, tape, CD-ROM, flash memory, etc. and other devices that store data using electrical, magnetic and optical recording means or others. Main memory 1020 also includes, in some embodiments, a video display memory for displaying images through a display device (not shown). Those skilled in the art will recognize that the memory system 1006 can comprise a variety of alternative components that have a variety of storage capacity.
    [0070] Where applicable, an input device 1010 and an output device 1012 can also be provided in the system as described in this document or in the modalities thereof. Input device 1010 can comprise any keyboard, mouse, physical transducer (for example a microphone) and can be interconnected to computer 1002 via an 1024 input interface, such as, for example, a graphical user interface, associated with or separate of the communication interface described above which includes the antenna interface for wireless communications. Output device 1012 may include a screen, a printer, a transducer (for example, a speaker), etc. and be interconnected to computer 1002 via an output interface 1026 which may include the communication interface described above which includes the antenna interface. Some devices, such as a network adapter or a modem, can be used as input and / or output devices.
    [0071] As is familiar to those skilled in the art, the computer system 1000 additionally includes an operating system and at least one application program. The operating system is the set of software that controls the operation of the computer system and the allocation of resources. The application program is the set of software that performs a desired task by the method of detecting an incipient error in a generator and / or any of the processes described above and process steps that use computer resources made available through the operating system.
    [0072] In accordance with the practices of people versed in the technique of computer programming, the present disclosure is described below with reference to the symbolic representations of the operations that are performed by the computer system 1000. Such operations are sometimes referred to as being performed by computer. It will be understood that the operations that are symbolically represented include the manipulation by the CPU 1004 of the electrical signals representing data bits and the maintenance of the data bits in the memory locations in the memory system 1006, as well as other signal processing. The memory locations where the data bits are kept are physical locations that have specific electrical, magnetic, or optical properties that correspond to the data bits. One or more modalities can be implemented in a tangible form in a program or programs defined by computer-executable instructions that can be stored in a computer-readable medium. The computer-readable medium can be any of the devices, or a combination of the devices, described above in conjunction with the 1006 memory system.
    [0073] As described in this document through the various modalities, a system and method for detecting an incipient failure in a generator is possible through the real-time monitoring of the magnetization inductance, winding resistances and the field transformation ratio for stator. Based on this PSO performance parameter estimation method within the current system and method, preventive maintenance measures can be taken into account before a forced generator drop is dictated. As described in this document, real-time estimation of generator performance parameters is preferred over the prior art since it does not require service interruption, such as partial load rejections required in the traditional approach.
    [0074] When describing the elements or attributes and / or the modalities of the same, the articles "one", "one", "the (a)" and "the said (a)" are intended to mean that there is one or more of the elements or attributes. The terms "that comprises (m)", "that includes (in)" and "that has (have)" are intended to be inclusive and mean that there may be additional elements or attributes in addition to those specifically described.
    [0075] Those skilled in the art will recognize that several changes can be made to the exemplary modalities and implementations described above without departing from the scope of the disclosure. Consequently, all matter contained in the description above or shown in the accompanying drawings must be interpreted as illustrative and not in a limiting sense.
    [0076] It should be further understood that the processes or steps as described in this document should not be interpreted as necessarily requiring their performance in the specific order discussed or illustrated. It should also be understood that additional or alternative processes and steps can be employed.
    权利要求:
    Claims (17)
    [0001]
    1. Method of detecting incipient failure of a generator in an electrical system CHARACTERIZED by the fact that it comprises the steps of: ensuring (200) at least one generator reference parameter of the generator for use as a baseline reference; measure (202) at least one generator operational parameter value; use (204) the at least one operational parameter value to solve an estimated present value of at least one of the generator's current performance parameters using the particle cloud optimization technique; and determining (206, 208) whether the estimated present values of at least one of the generator's current performance parameters are outside an acceptable limit.
    [0002]
    2. Method according to claim 1, CHARACTERIZED by the fact that the step of determining whether the estimated present values of at least one of the generator's current performance parameters are outside an acceptable limit further comprises the step of ensuring , for each generator setpoint, a tolerance value of the corresponding generator setpoint and determine whether the estimated present values of the generator current performance parameter are within a range between the setpoint plus the tolerance value and the reference value minus the tolerance value.
    [0003]
    3. Method, according to claim 1, CHARACTERIZED by the fact that the step of determining whether the estimated present values of at least one of the generator's current performance parameters are outside an acceptable limit comprises the steps of: comparing (206) the estimated value of at least one of the generator's current performance parameters to a corresponding value of one of the generator reference parameters; and if the estimated value is not an acceptable value, provide a warning (210) that an estimated value of at least one of the generator's current performance parameters is unacceptable.
    [0004]
    4. Method according to claim 1, CHARACTERIZED by the fact that, if, during a stage of determining whether the estimated present values of at least one of the generator's current performance parameters are outside an acceptable limit, it is determined that the estimated present values of all current generator performance parameters are within an acceptable limit, perform the step of waiting for a predetermined period of time and repeat the steps, as defined in claim 1.
    [0005]
    5. Method, according to claim 1, CHARACTERIZED by the fact that the step of using at least one operational parameter value to solve an estimated present value of at least one among the current generator performance parameters with the use The particle cloud optimization technique additionally comprises the steps of: treating (701) each estimated value of at least one of the current performance parameters of the adjusted generator as a particle, where each set of estimated values is a different position of the particle; and for each new set of operational parameters: 1. initialize (702) each particle position, 2. determine (704) an initial value for the dynamic voltage of quadrature generator and direct axis; 3. calculate a response (706) of the generator model with a selected particle position using the received operating field voltage, 4. determine a suitability assessment (708) of the particle to find a better eigenvalue and a better value and 5. update (712) a position and speed for each particle based on the best eigenvalue and the best global value determined.
    [0006]
    6. Method, according to claim 1, CHARACTERIZED by the fact that the step of using at least one operational parameter value to solve an estimated present value of at least one among the current generator performance parameters with the use the particle cloud optimization technique additionally comprises the steps of: treating each estimated value of at least one of the current generator performance parameters adjusted as a particle, where each set of estimated values is a different position of the particle; and for each new set of operational parameters: Step 0: initialize signs of iteration, NOI = 0, J = 0 Step 1: initialize each particle position according to the equation:
    [0007]
    7. Method, according to claim 1, CHARACTERIZED by the fact that the generator reference parameters comprise at least one of: power output and nominal voltage of the generator, Ra = resistance of the armature winding, Rfd = winding resistance field, Xl = leakage reactance, Xd = steady-state direct-axis synchronous reactance, Xq = steady-state quadrature synchronous reactance, X'd = synchronous direct-axis reactance, X'q = dynamic quadrature synchronous reactance, T'do = open circuit direct axis time constant of the generator or T'qo = open circuit quadrature time constant of the generator.
    [0008]
    8. Method, according to claim 1, CHARACTERIZED by the fact that the operational parameters of the generator comprise at least one of: Efd = field voltage, Ifd = field current, Et = terminal voltage, It = terminal current , Φ = the load angle, δi = the energy angle (internal rotor angle), Vgen = generator voltage, Vr = reference voltage, P = Actual generator energy, or Q = Reactive generator energy.
    [0009]
    9. Method, according to claim 1, CHARACTERIZED by the fact that the current performance parameters of the generator comprise at least one of: Xd = steady-state direct-axis synchronous reactance, Xq = steady-state quadrature synchronous reactance , X'd = direct axis synchronous reactance, X'q = dynamic quadrature synchronous reactance, T'do = open circuit direct axis time constant of the generator, or T'qo = open circuit quadrature time constant of the generator.
    [0010]
    10. System for detecting an incipient failure of a generator (12) in an electrical system (10) CHARACTERIZED by the fact that it comprises: a processor (1004) operatively coupled to the memory (1006), an input data interface ( 1024) to receive operating parameters associated with the generator and a user interface (1010, 1012), the processor is for executing instructions executable by computer and the instructions executable by computer are structured to: guarantee (200) at least one parameter of generator reference of the generator for use as a baseline reference; measure (202) at least one generator operational parameter value; use (204) the at least one operational parameter value to solve an estimated present value of at least one of the generator's current performance parameters using the particle cloud optimization technique; and determining (206, 208) whether the estimated present values of at least one of the generator's current performance parameters are outside an acceptable limit.
    [0011]
    11. System according to claim 10, CHARACTERIZED by the fact that structured computer executable instructions to determine if the estimated present values of at least one of the generator's current performance parameters are outside an acceptable limit are additionally structured to ensure, for each generator setpoint, a tolerance value of the corresponding generator setpoint and determine whether the estimated present values of the generator current performance parameters are within a range between the setpoint plus the tolerance value and the reference value minus the tolerance value.
    [0012]
    12. System according to claim 10, CHARACTERIZED by the fact that the computer executable instructions structured to determine whether the estimated present values of at least one of the generator's current performance parameters are outside an acceptable limit are additionally structured to: compare (206) the estimated value of at least one of the generator's current performance parameters to a corresponding value of the generator reference parameters; and if the estimated value is not an acceptable value, provide a warning (210) that an estimated value of at least one of the generator's current performance parameters is unacceptable.
    [0013]
    13. System according to claim 10, CHARACTERIZED by the fact that computer executable instructions structured to use at least one operational parameter value to solve an estimated present value of at least one of the generator's current performance parameters using the particle cloud optimization technique, they are additionally structured to: treat each estimated value of at least one of the current performance parameters of the adjusted generator as a particle, where each set of estimated values is a different position from particle; and for each new set of operational parameters, the computer executable instructions are additionally structured to: a. initialize each particle position; B. determine an initial value for the dynamic voltage of quadrature generator and direct axis; ç. calculate a generator model response with a selected particle position using the received operating field voltage, d. determining a suitability assessment of the particle to find a better eigenvalue and a better overall value, e. update a position and velocity for each particle based on the best eigenvalue and the best global value determined.
    [0014]
    14. System according to claim 10, CHARACTERIZED by the fact that computer executable instructions structured to use at least one operational parameter value to solve an estimated present value of at least one of the generator's current performance parameters with the use of the particle cloud optimization technique, it is additionally structured to: treat each estimated value of at least one of the current performance parameters of the adjusted generator as a particle, where each set of estimated values is a different position from particle; and for each new set of operational parameters, the computer executable instructions are additionally structured to: a. initialize signs of iteration, NOI = 0, J = 0 b. initialize each particle position according to the equation:
    [0015]
    15. System, according to claim 10, CHARACTERIZED by the fact that the generator reference parameters comprise at least one of: power output and nominal voltage of the generator, Ra = resistance of the armature winding, Rfd = winding resistance field, Xl = leakage reactance, Xd = steady-state direct-axis synchronous reactance, Xq = steady-state quadrature synchronous reactance, X'd = synchronous direct-axis reactance, X'q = dynamic quadrature synchronous reactance, T'do = open circuit direct axis time constant of the generator or T'qo = open circuit quadrature time constant of the generator.
    [0016]
    16. System, according to claim 10, CHARACTERIZED by the fact that the operational parameters of the generator comprise at least one among: Efd = field voltage, Ifd = field current, E t = terminal voltage, It = terminal, Φ = the load angle, δi = the energy angle (internal rotor angle), Vgen = generator voltage, Vr = reference voltage, P = Actual generator energy, or Q = Reactive generator energy.
    [0017]
    17. System according to claim 10, CHARACTERIZED by the fact that the current performance parameters of the generator comprise at least one of: Xd = steady-state direct-axis synchronous reactance, Xq = steady-state quadrature synchronous reactance , X'd = direct axis synchronous reactance, X'q = dynamic quadrature synchronous reactance, T'do = open circuit direct axis time constant of the generator, or T'qo = open circuit quadrature time constant of the generator.
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    同族专利:
    公开号 | 公开日
    WO2013070736A1|2013-05-16|
    US9753096B2|2017-09-05|
    PL2798734T3|2018-12-31|
    US20150039252A1|2015-02-05|
    EP2798734A1|2014-11-05|
    HUE040514T2|2019-03-28|
    EP2798734A4|2016-07-06|
    BR112014010913A2|2017-05-16|
    ES2681045T3|2018-09-11|
    EP2798734B1|2018-07-04|
    US8866626B2|2014-10-21|
    US20120050053A1|2012-03-01|
    引用文献:
    公开号 | 申请日 | 公开日 | 申请人 | 专利标题
    JP2563894B2|1982-09-25|1996-12-18|株式会社東芝|Multi-input / output sample value PID controller|
    US5124626A|1990-12-20|1992-06-23|Mts Systems Corporation|Sinusoidal signal amplitude and phase control for an adaptive feedback control system|
    US5294879A|1991-11-01|1994-03-15|Basler Electric Company|Microprocessor-controlled regulator|
    US5691896A|1995-08-15|1997-11-25|Rosemount, Inc.|Field based process control system with auto-tuning|
    US6549858B1|1998-02-03|2003-04-15|Sheet Dynamics, Ltd.|Structural control and monitoring using adaptive spatio-temporal filtering|
    FI109847B|1998-06-11|2002-10-15|Abb Oy|Procedure for minimizing the error in a synchronous motor rotor angle estimate|
    US6216066B1|1998-07-01|2001-04-10|General Electric Company|System and method for generating alerts through multi-variate data assessment|
    US6516309B1|1998-07-17|2003-02-04|Advanced Research & Technology Institute|Method and apparatus for evolving a neural network|
    US6125077A|1999-04-20|2000-09-26|Oak Technology, Inc.|Apparatus and method for providing a quiet time before analog signal sampling in a mixed signal integrated circuit employing synchronous and asynchronous clocking|
    US6760716B1|2000-06-08|2004-07-06|Fisher-Rosemount Systems, Inc.|Adaptive predictive model in a process control system|
    US7113834B2|2000-06-20|2006-09-26|Fisher-Rosemount Systems, Inc.|State based adaptive feedback feedforward PID controller|
    US6577908B1|2000-06-20|2003-06-10|Fisher Rosemount Systems, Inc|Adaptive feedback/feedforward PID controller|
    US7024253B2|2000-08-21|2006-04-04|Honeywell International Inc.|Auto-tuning controller using loop-shaping|
    US6697767B2|2000-10-18|2004-02-24|The National University Of Singapore|Robust process identification and auto-tuning control|
    US6700356B1|2000-10-24|2004-03-02|Kohler Co.|Method and apparatus for regulating the excitation of an alternator of a genset|
    US7317953B2|2003-12-03|2008-01-08|Fisher-Rosemount Systems, Inc.|Adaptive multivariable process controller using model switching and attribute interpolation|
    US20050137834A1|2003-12-18|2005-06-23|Heydt Gerald T.|System and method of estimating synchronous generator parameters|
    US7064526B2|2004-04-23|2006-06-20|Astronics Advanced Electronic Systems Corp.|Fault tolerant architecture for permanent magnet starter generator subsystem|
    US8131656B2|2006-01-31|2012-03-06|The Board Of Trustees Of The University Of Illinois|Adaptive optimization methods|
    US8190299B2|2006-07-19|2012-05-29|Rovnyak Steven M|Integrated and optimized distributed generation and interconnect system controller|
    US7598751B2|2006-08-14|2009-10-06|Clemson University Research Foundation|Impedance-based arc fault determination device and method|
    US7853805B1|2007-02-02|2010-12-14|Hrl Laboratories, Llc|Anti-tamper system|
    US8275488B2|2008-01-31|2012-09-25|Basler Electric Co.|Digital excitation control system utilizing swarm intelligence and an associated method of use|
    US8024682B2|2008-03-03|2011-09-20|Solido Design Automation Inc.|Global statistical optimization, characterization, and design|
    US8346693B2|2009-11-24|2013-01-01|King Fahd University Of Petroleum And Minerals|Method for hammerstein modeling of steam generator plant|
    US8620631B2|2011-04-11|2013-12-31|King Fahd University Of Petroleum And Minerals|Method of identifying Hammerstein models with known nonlinearity structures using particle swarm optimization|US20130331963A1|2012-06-06|2013-12-12|Rockwell Automation Technologies, Inc.|Systems, methods, and software to identify and present reliability information for industrial automation devices|
    DE102013200872A1|2013-01-21|2014-07-24|Areva Gmbh|Method and device for detecting errors in an electrical machine|
    US9574511B2|2014-07-24|2017-02-21|Basler Electric Company|System and method for a load anticipation feature and its tuning method for a generating set|
    CN104682405B|2015-03-31|2018-01-12|福州大学|A kind of var Optimization Method in Network Distribution based on taboo particle cluster algorithm|
    EP3288139A4|2015-04-24|2018-11-14|Hitachi, Ltd.|Power system stabilization system and method|
    CN105762773A|2016-03-22|2016-07-13|国网浙江省电力公司丽水供电公司|Power distribution network user fault automatic optimization discrimination method|
    CN106229964A|2016-07-22|2016-12-14|南京工程学院|A kind of based on the electrical power distribution network fault location method improving binary particle swarm algorithm|
    CN106503346B|2016-10-31|2019-02-26|浙江大学|A kind of generating set electromagnetical transient emulation method of quick and smooth starting|
    CN106875063A|2017-02-21|2017-06-20|集美大学|A kind of dynamic positioning ship energy management optimization method|
    CN106953577B|2017-03-20|2019-08-09|福州大学|A kind of non-synchronous motor parameter identification method based on Modified particle swarm optimization algorithm|
    CN107450563B|2017-09-21|2020-08-25|景德镇陶瓷大学|Self-adaptive information feedback particle swarm robot path selection method based on multiple subgroups|
    EP3480610B1|2017-11-07|2020-04-08|Siemens Gamesa Renewable Energy A/S|Diagnosing a winding set of a stator|
    CN107863910B|2017-12-21|2020-05-22|浙江工业大学|Permanent magnet synchronous motor optimal fractional order PID control method with strong tracking performance|
    GB201808798D0|2018-05-30|2018-07-11|Rolls Royce Plc|Angle determination for a generator|
    GB201808796D0|2018-05-30|2018-07-11|Rolls Royce Plc|Power system|
    GB201808797D0|2018-05-30|2018-07-11|Rolls Royce Plc|Power system|
    CN110266230A|2019-01-17|2019-09-20|浙江工业大学|A kind of parameter identification method of parallel mixed electrical automobile power permanent magnet synchronous motor|
    US11012016B2|2019-10-14|2021-05-18|Schweitzer Engineering Laboratories, Inc.|Energy packet control of generator prime mover|
    US11146193B2|2019-10-14|2021-10-12|Schweitzer Engineering Laboratories, Inc.|Genset engine paralleling controls, devices, systems, and methods|
    法律状态:
    2018-12-04| B06F| Objections, documents and/or translations needed after an examination request according art. 34 industrial property law|
    2019-10-15| B06U| Preliminary requirement: requests with searches performed by other patent offices: suspension of the patent application procedure|
    2021-01-05| B09A| Decision: intention to grant|
    2021-02-02| B16A| Patent or certificate of addition of invention granted|Free format text: PRAZO DE VALIDADE: 20 (VINTE) ANOS CONTADOS A PARTIR DE 07/11/2012, OBSERVADAS AS CONDICOES LEGAIS. |
    优先权:
    申请号 | 申请日 | 专利标题
    US13/290,910|US8866626B2|2008-01-31|2011-11-07|System and method for detecting generator incipient failures|
    US13/290,910|2011-11-07|
    PCT/US2012/063910|WO2013070736A1|2011-11-07|2012-11-07|System and method for detecting generator incipient failures|
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