Patent Description:
DC link capacitors are a cornerstone in power conversion design for many applications, including three-phase Pulse Width Modulation (PWM) inverters, photovoltaic and wind power inverters, industrial motor drives, vehicle onboard chargers and inverters, medical equipment power supplies, etc. Various demanding applications possess cost, harsh environmental, and stringent reliability constraints. Although circuit designs can use different approaches, the long-standing core of power conversion designs includes DC-Link capacitors. DC-Link capacitors can improve system energy density and resolve the physical challenges of ripples introduced by rapid switching that is inherent to switching power conversions.

Ideally, the input power supplied to a three-phase power distribution system used in a power conversion system would be constant and balanced. However, a variety of power quality disturbances exist today, including harmonic distortion, frequency variation, noise, transient voltage spikes, outages, and voltage surges and sags. Unbalanced voltages typically inject a second harmonic voltage component on to the DC bus voltage that increases electrical stresses on the DC link capacitor, potentially shortening the capacitor lifetime. <CIT> relates to a motor control device comprising a capacitor lifetime prediction unit. The lifetime prediction is based on a comparison between the measured capacitance value and an end-of-life reference capacitance value.

<CIT> discloses a capacitor Remaining Useful Life (RUL) estimation unit, which is configured to calculate an Expired Life based on two measured capacitor state variables, being a capacitor voltage and a capacitor temperature, and determine the RUL by calculating the difference between a Projected Life and the Expired Life.

The invention is defined by independent method claim <NUM> and independent system claim <NUM>. Preferred embodiments are described by the dependent claims.

An object of the present disclosure provides a method for capacitor health monitoring. Systems and methods of the present disclosure may evaluate DC link capacitor parameters to determine the remaining life of the DC link capacitor before failure. The method comprises analyzing a first set of DC link capacitor state variables to determine a first probability of failure, analyzing at least one more set, such as a second, third, fourth, etc. of DC link capacitor state variables to determine a second, third, fourth, etc. probability of failure, calculating an average of probabilities to determine a final probability of failure of the DC link capacitor, and comparing the final probability with a predetermined curve corresponding to a DC link capacitor lifespan, and determining a remaining lifespan of the DC link capacitor using the curve.

The disclosed algorithm for prognostics and health monitoring can be implemented as on-board monitoring loaded onto existing hardware (e.g. processors) and using existing feedback signals, without adding extra hardware to the existing system. In various embodiments, the disclosed algorithm is implemented solely in software, utilizing existing hardware systems.

With reference to <FIG>, an example of an architecture of a DC link capacitor health monitoring system is illustrated that is included in a three-phase inverter having a diode rectifier front end, in accordance with various embodiments. The power converter is mainly composed of a three-phase inverter <NUM>, a DC link capacitor <NUM>, a diode front end rectifier <NUM>, and a controller <NUM>.

In various embodiments, the DC link capacitor <NUM> comprises a single DC link capacitor. In various embodiments, the DC link capacitor <NUM> comprises a plurality of capacitors, thus forming a capacitor bank.

During operation, the power converter receives three-phase alternative electric power <NUM> and drives a load <NUM>. The load <NUM> may comprise, for example, a three-phase motor.

In various embodiments, the input current (IIN), input voltage (VIN), DC link voltage (VDC), capacitor input current (Ic), output current (IOUT), output voltage (VOUT), loading power (PLOAD), and/or DC link capacitor temperature (T) are monitored.

In various embodiments, the controller <NUM> includes one or more controllers (e.g., processors) and one or more tangible, non-transitory memories capable of implementing digital or programmatic logic. In various embodiments, for example, the one or more controllers are one or more of a general purpose processor, digital signal processor (DSP), application specific integrated circuit (ASIC), field programmable gate array (FPGA), or other programmable logic device, discrete gate, transistor logic, or discrete hardware components, or any various combinations thereof or the like.

With reference to <FIG>, a control logic for controller <NUM> for a DC link capacitor health monitoring system is illustrated, in accordance with various embodiments. System program instructions and/or controller instructions may be loaded onto a non-transitory, tangible computer-readable medium having instructions stored thereon that, in response to execution by a controller, cause the controller to perform various operations.

In various embodiments, controller <NUM> is an existing controller used for a conversion system, such as the conversion system depicted in <FIG> for example. In this regard, system program instructions, as disclosed herein, may be loaded onto an existing controller for a power conversion system, without the need for any additional hardware. In this regard, the methods and control logic disclosed herein may be retrofitted into an existing power conversion system.

In general, controller <NUM> may receive a plurality of capacitor state variables, calculate a probability of failure of a DC link capacitor for each of the capacitor state variables, determine a final probability of failure based on the plurality of calculated probabilities (e.g., by taking an average value), and then determining a remaining lifespan of the DC link capacitor based on the final probability of failure. Furthermore, a plurality of values may be provided for each capacitor state variable, by which each probability of failure is determined.

In this regard, controller <NUM> may receive a plurality of capacitor state variables including input current (IIN) (also referred to herein as state variable <NUM>), input voltage (VIN) (also referred to herein as state variable <NUM>), DC link voltage (VDC) (also referred to herein as state variable <NUM>), capacitor input current (IC) (also referred to herein as state variable <NUM>), output current (IOUT) (also referred to herein as state variable <NUM>), output voltage (VOUT) (also referred to herein as state variable <NUM>), loading power (PLOAD) (also referred to herein as state variable <NUM>), and/or DC link capacitor temperature (T) (also referred to herein as state variable <NUM>). A decision model may be implemented for each capacitor state variable for determining a health status using a probability of failure of the DC link capacitor based on each of the capacitor state variables. In this regard, decision model <NUM> may utilize a plurality of state variables <NUM> to determine a first probability of failure of the DC link capacitor. Decision model <NUM> may utilize a plurality of state variables <NUM> to determine a second probability of failure of the DC link capacitor. Decision model <NUM> may utilize a plurality of state variables <NUM> to determine a third probability of failure of the DC link capacitor. Decision model <NUM> may utilize a plurality of state variables <NUM> to determine a fourth probability of failure of the DC link. Decision model <NUM> may utilize a plurality of state variables <NUM> to determine a fifth probability of failure of the DC link capacitor. Decision model <NUM> may utilize a plurality of state variables <NUM> to determine a sixth probability of failure of the DC link capacitor. Decision model <NUM> may utilize a plurality of state variables <NUM> to determine a seventh probability of failure of the DC link capacitor. Decision model <NUM> may utilize a plurality of state variables <NUM> to determine an eighth probability of failure of the DC link capacitor.

In various embodiments, at node <NUM>, controller <NUM> may calculate a final probability of failure <NUM> of the DC link capacitor. In various embodiments, the final probability of failure <NUM> is calculated by taking the average value of the probability of failures calculated by each of the decision models (e.g., decision model <NUM> through decision model <NUM>). The final probability of failure <NUM> may then be used to estimate a remaining lifespan, or a failure time, of the DC link capacitor.

With reference to <FIG>, a decision model <NUM> is illustrated, in accordance with various embodiments. Decision model <NUM>, decision model <NUM>, decision model <NUM>, decision model <NUM>, decision model <NUM>, decision model <NUM>, decision model <NUM>, and/or decision model <NUM> of <FIG> may be similar to decision model <NUM>. Decision model <NUM> may receive a plurality of state variables. Decision model <NUM> may receive a plurality of first DC link capacitor state variables, such as input current (IIN) for example. In various embodiments, the plurality of first DC link capacitor state variables are an array of values as a function of time. Decision model <NUM> may determine, in turn, whether each of the plurality of first DC link capacitor state variables are less than a threshold value <NUM>. The threshold value <NUM> is a predetermined value, in accordance with various embodiments. Furthermore, although described herein as determining whether each of the plurality of first DC link capacitor state variables are less than a threshold value, decision model <NUM> may conversely determine whether each of the plurality of first DC link capacitor state variables are greater than a threshold value without departing from the scope of the present disclosure. For each of the capacitor state variables that are less than the threshold value, the decision model <NUM> determines a deviation (i.e., difference) of the capacitor state variable from the threshold value <NUM>. The decision model <NUM> then determines whether this deviation happened with a predetermined threshold time <NUM>. As an example, when analyzing input current (IIN), the threshold value may be in units of amperes, such as <NUM> amperes for example. However, the threshold value may be any value and further may vary depending on the status of operation of the power conversion system. Continuing with the example, if the capacitor state variable is <NUM> amperes, the threshold value <NUM> is <NUM> amperes, and the threshold time <NUM> is <NUM> seconds, then the decision model <NUM> may calculate the deviation of the state variable from the threshold value as <NUM> amperes (<NUM> amperes - <NUM> amperes). The decision model <NUM> may then determine whether the deviation from <NUM> amperes occurred within <NUM> seconds. In this example, the decision model <NUM> may subtract the time (<NUM>) that the state variable was at the threshold value (i.e., <NUM> amperes) from the time (t2) at which the state variable reached <NUM> amperes to determine whether this deviation from the threshold value <NUM> was within the threshold time <NUM> of <NUM> seconds. In response to this deviation being calculated to have happened within the threshold time <NUM>, then the capacitor state variable counts towards a higher probability of failure (P1). In response to this deviation being calculated to have not happened within the threshold time <NUM>, then the capacitor state variable counts against a higher probability of failure (P2). After all of the plurality of first DC link capacitor state variables have been analyzed, an individual (e.g., a first) probability of failure corresponding to the first DC link capacitor state variable is determined by the following equation:
<MAT> where P1 is the probability of failure due to the number of deviations of the first DC link capacitor state variables that happened within the threshold time, and P2 is the probability of failure due to the number of deviations of the first DC link capacitor state variables that happened outside of the threshold time.

In various embodiments, the threshold value <NUM> and threshold time <NUM> may be predetermined during a training phase of the decision model <NUM>. For example, during the training phase of the decision models, the values of respective parameters can be used to check which threshold values provide the most accurate probability of failure predictions. For example, considering a decision model for input current, during the training phase all the values of the input current and the respective time may be used to check which provides the best threshold for accurately predicting probability of failure. In various embodiments, Gini index (Gi) may be defined:
<MAT> where 'p' is the probability of failure.

Each value in the training dataset (i.e. input current and the corresponding time) may be associated with this Gini Index and the one with the lowest Gini index value may be used as the threshold value and threshold time. In this manner, the most optimal threshold values may be used for determining probability. However, it is contemplated herein that various other methods for determining threshold values may be used without departing from the scope of the present disclosure.

With reference again to <FIG>, controller <NUM> may iterate through the logic of decision model <NUM> (see <FIG>) for each of the DC link capacitor state variables. After an individual probability of failure is calculated for each of the DC link capacitor state variables, controller <NUM> may calculate a final probability of failure <NUM> of the DC link capacitor. The final probability of failure <NUM> of the DC link capacitor may be calculated by taking an average value of the individual probabilities of failure of the DC link capacitor values. The final probability of failure <NUM> of the DC link capacitor may then be used to estimate the remaining lifespan, or a failure time, of the DC link capacitor using profile mapper <NUM>. Profile mapper <NUM> may utilize a predetermined curve, such as the cumulative distribution function of capacitor failure curve represented in <FIG> for example, to estimate the remaining lifespan.

With reference to <FIG>, a curve <NUM> representing the cumulative distribution function of capacitor failure versus time is provided. The cumulative distribution function (CDF) may be calculated as:
<MAT> where:
<MAT>
<MAT>
<MAT> where:.

The obtained graph is depicted in <FIG>. The curve <NUM> can be represented in matrix form (2D) and may be stored in memory. As illustrated, the maximum value of curve <NUM> is <NUM>, which represents <NUM>% probability of failure of the DC link capacitor (region II), and the minimum value of curve <NUM> is <NUM>, which represents <NUM>% probability of failure of the DC link capacitor. If the final probability of failure <NUM> of the DC link capacitor is less than <NUM>% (e.g., ≤ <NUM>) (region I), then the failure time can be predicted. The remaining lifespan of the DC link capacitor may be determined by fitting the final probability of failure <NUM> of the DC link capacitor to curve <NUM>, thereby providing an estimated remaining lifespan of the DC link capacitor. For example, if the final probability of failure <NUM> of the DC link capacitor is <NUM> (T1'), then the estimated remaining lifespan would be calculated as T2'-T1'.

With reference to <FIG>, a method <NUM> for monitoring DC link capacitor health is provided, in accordance with various embodiments. Method <NUM> includes receiving, by a controller, a plurality of first DC link capacitor state variables (step <NUM>). Method <NUM> includes determining, by the controller, whether each of the first DC link capacitor state variables is less than a first threshold value (step <NUM>). Method <NUM> includes calculating, by the controller, a deviation of each of the first DC link capacitor state variables from the first threshold value, in response to the first DC link capacitor state variable being less than the first threshold value (step <NUM>). Method <NUM> includes determining, by the controller, whether each of the deviations of the first DC link capacitor state variables occurred within a first threshold time (step <NUM>). Method <NUM> includes calculating, by the controller, a first probability of failure of a DC link capacitor by dividing a total number of the deviations of the first DC link capacitor state variables that occurred within the first threshold time by a total number of the plurality of first DC link capacitor values (step <NUM>). Method <NUM> includes repeating steps <NUM>-<NUM> for a plurality of second DC link capacitor state variables (step <NUM>). Method <NUM> includes calculating, by the controller, a final probability of failure of the DC link capacitor (step <NUM>). Method <NUM> includes estimating, by the controller, a remaining lifespan of the DC link capacitor (step <NUM>).

With combined reference to <FIG>, step <NUM> may include receiving, by controller <NUM>, a plurality of first DC link capacitor state variables (e.g., one of state variables <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, or <NUM>). The state variables may be received from a sensor, memory, or may be derived from another variable. Step <NUM> may include determining, by controller <NUM>, whether each of the first DC link capacitor state variables is less than a first threshold value (e.g., threshold value <NUM> of <FIG>). Step <NUM> may include calculating, by controller <NUM>, a deviation of each of the first DC link capacitor state variables from the first threshold value, in response to the first DC link capacitor state variable being less than the first threshold value. Step <NUM> may include determining, by controller <NUM>, whether each of the deviations of the first DC link capacitor state variables occurred within a first threshold time (e.g., threshold time <NUM> of <FIG>). Step <NUM> may include calculating, by controller <NUM>, a first probability of failure of a DC link capacitor using equation <NUM> as provided herein. Step <NUM> may include repeating steps <NUM> through <NUM> using a plurality of second DC link capacitor state variables (e.g., one of state variables <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, or <NUM>) that are different from the plurality of first DC link capacitor state variables. For example, step <NUM> may include receiving a plurality of input current (IIN) values and step <NUM> may include receiving a plurality of input voltage (VIN) values. Step <NUM> may be repeated for a number of times, as desired, until all of the desired variables are analyzed, including DC link voltage (VDC), capacitor input current (IC), output current (IOUT), output voltage (VOUT), loading power (PLOAD), and/or DC link capacitor temperature (T). Step <NUM> may include calculating, by controller <NUM>, final probability of failure <NUM> of DC link capacitor <NUM> (see <FIG>). Step <NUM> may include estimating, by controller <NUM>, a remaining lifespan of the DC link capacitor using profile mapper <NUM> as described herein.

Systems, and methods are provided herein. In the detailed description herein, references to "one embodiment", "an embodiment", "various embodiments", etc., indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may not necessarily include the particular feature, structure, or characteristic.

Claim 1:
A method (<NUM>) for monitoring DC link capacitor (<NUM>) health, comprising
receiving (<NUM>), by a controller (<NUM>), a plurality of first DC link capacitor state variables (<NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>);
determining (<NUM>), by the controller, whether each of the first DC link capacitor state variables is less than a first threshold value;
calculating (<NUM>), by the controller, a deviation of each of the first DC link capacitor state variables from the first threshold value, in response to the first DC link capacitor state variable being less than the first threshold value;
determining (<NUM>), by the controller, whether each of the deviations of the first DC link capacitor state variables occurred within a first threshold time;
calculating (<NUM>), by the controller, a first probability of failure of the DC link capacitor by dividing a total number (P1) of the deviations of the first DC link capacitor state variables that occurred within the first threshold time by a total number (P1+P2) of the deviations of the first DC link capacitor state variables;
receiving, by the controller, a plurality of second DC link capacitor state variables (<NUM>);
determining, by the controller, whether each of the second DC link capacitor state variables is less than a second threshold value;
calculating, by the controller, a deviation of each of the second DC link capacitor state variables from the second threshold value, in response to the second DC link capacitor state variable being less than the second threshold value;
determining, by the controller, whether each of the deviations of the second DC link capacitor state variables occurred within a second threshold time; and
calculating, by the controller, a second probability of failure of a DC link capacitor by dividing a total number (P1) of the deviations of the second DC link capacitor state variables that occurred within the second threshold time by a total number (P1+P2) of the
deviations of the second DC link capacitor state variables;
calculating (<NUM>), by the controller, a final probability of failure by calculating a mean value of the first probability of failure and the second probability of failure; and
estimating (<NUM>), by the controller, a remaining lifespan of the DC link capacitor.