CLAIM OF PRIORITY 
       [0001]    This patent claims priority to U.S. provisional patent application Ser. No. 61/490,673, which was filed on May 27, 2011, and is entitled “DIAGNOSTICS OF INTEGRATED SOLAR POWER,” the entire disclosure of which is expressly incorporated by reference herein. 
     
    
     TECHNICAL FIELD 
       [0002]    The present description generally relates to power generation and particularly to power generation by solar photovoltaic and power electronic converters. 
       BACKGROUND 
       [0003]    In recent years, due to growing global energy needs, sources of energy alternative to fossil fuel have gained significant popularity. One such energy source has been solar power that converts energy emitted by the sun into electricity and other useful forms of energy. One common device for converting solar energy into electricity is the photovoltaic (PV) solar cell. In one common form, a PV cell includes a semiconductor material, such as silicon, that is configured to form a p-n junction. Photons present in solar energy strike the solar cell, and photons having energy levels that exceed a band gap of the material forming the solar cell liberate electrons from the silicon atoms. A silicon atom with a missing electron has a positive charge referred to as a “hole.” The electrons and holes seek to recombine in the solar cell, and the recombination process generates an electrical voltage and current that can be used in electrical power generation. 
         [0004]    An individual PV solar cell typically produces an output voltage of approximately 0.5-0.6 V and an output current of approximately 1.0-2.0 A when exposed to strong sunlight. In most commercial embodiments, many individual PV solar cells are arranged in a single panel and are electrically connected together to enable the panel to generate a greater amount of electrical power. Larger solar power generation facilities typically incorporate solar power arrays that include many panels with the entire facility employing thousands or even millions of the individual PV cells. Large scale solar power generation facilities can cover multiple hectares or even multiple square kilometers of land with PV solar panels. 
         [0005]    Many solar power generation systems include a power converter that is electrically connected to one or more solar panels. The power converter is used to condition and regulate the electrical power supplied by the panels into an electrical power useful for powering various devices or for transmission via a power grid. Typical PV panels produce direct current (DC) electrical power. One type of power converter converts the DC power supplied by the PV panel into an alternating current (AC) form that is supplied to an electrical grid for delivery to remote locations or the AC current may be directly connected to one or more commonly used electrical appliances to operate the appliances. 
         [0006]    One challenge to continued growth in the field of solar power concerns monitoring of a large number of PV panels and power converter units that are electrically connected to a large power distribution network, such as the electrical grid. Large-scale solar power generation facilities include monitoring equipment that is designed to detect and compensate for panel failures, power surges, brown-outs, and other negative events that could ramify beyond the solar power generation facility and have negative effects on the larger electrical grid. In contrast, micro-generation of solar power involves connecting a much larger number of small solar power generation facilities to the electrical grid. Examples of micro-generation facilities include residential solar power installations that typically include tens or hundreds of square meters of solar panels. Micro-generation facilities often sell excess electrical power to an electrical utility company. Existing monitoring systems are not equipped to identify that occur in micro-generation solar power systems quickly and accurately. Additionally, the costs associated with existing monitoring systems present a barrier to their use with micro-generation systems. Consequently, improvements to monitoring of solar power generation systems that enable fast diagnosis of faults without requiring extensive monitoring equipment would be beneficial. 
       SUMMARY 
       [0007]    In one embodiment, a method for identifying operational modes of a solar power system has been developed. The method includes measuring at least one operational parameter of a solar cell, measuring an output of the solar cell, generating a first estimated output for the solar cell with reference to a first model of the solar cell operating in a first operating mode with the at least one operational parameter, generating a second estimated output for the solar cell with reference to a second model of the solar cell operating in a second operating mode with the at least one operational parameter, generating a first probability value that the solar cell is operating in the first operating mode, the first probability value being generated with reference to the first estimated output of the solar cell and a difference between the first estimated output of the solar cell and the measured output of the solar cell, generating a second probability value that the solar cell is operating in the second operating mode, the second probability value being generated with reference to the second estimated output of the solar cell and a difference between the second estimated output of the solar cell and the measured output of the solar cell, identifying a current operating mode of the solar cell as being only one of the first operating mode or the second operating mode, the current operating move being identified with reference to a previous operating mode, the first probability value, and the second probability value, and disconnecting the output of the solar cell from a load in response to the identified current operating mode being the second operating mode. 
         [0008]    In another embodiment a method for identifying operating modes of a solar power system has been developed. The method includes measuring at least one operational parameter of a solar cell, measuring an output of the solar cell, measuring at least one operational parameter of a power converter that is electrically connected to the solar cell, measuring an output of the power converter, generating a first estimated output for the solar cell with reference to a first model of the solar cell operating in a first operating mode with the at least one operational parameter of the solar cell, generating a second estimated output for the solar cell with reference to a second model of the solar cell operating in a second operating mode with the at least one operational parameter of the solar cell, generating a first probability value that the solar cell is operating in the first operating mode, the first probability value being generated with reference to the first estimated output of the solar cell and a difference between the first estimated output of the solar cell and the measured output of the solar cell, generating a second probability value that the solar cell is operating in the second operating mode, the second probability value being generated with reference to the second estimated output of the solar cell and a difference between the second estimated output of the solar cell and the measured output of the solar cell, identifying a current operating mode of the solar cell as being only one of the first operating mode or the second operating mode, the current operating move being identified with reference to a previous operating mode, the first probability value, and the second probability value, generating a plurality of estimated outputs for the power converter with reference to a corresponding plurality of models of the power converter, each model in the plurality of models corresponding to one operating mode in a plurality of operating modes of the power converter, generating a plurality of probability values, each probability value in the plurality of probability values being a probability that the power converter is operating in one of the plurality of operating modes of the power converter, each probability value being generated with reference to a corresponding one of the plurality of estimated outputs of the power converter and a difference between the one estimated output and the measured output of the power converter, identifying a current operating mode of the power converter with reference to a previous operating mode, and each of the plurality of probability values, comparing the identified current operating mode of the power converter to an expected operating mode of the power converter with reference to a predetermined number of power converter operating modes having a predetermined order, and disconnecting the output of the power converter from a load in response to at least one of the identified current operating mode of the solar cell being the second operating mode or the identified current operating mode of the power converter being different from the expected operating mode. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0009]      FIG. 1  is a schematic diagram of a solar power generation system that is configured to be monitored for faults during operation. 
           [0010]      FIG. 2  is a diagram of a multiple-model adaptive estimation system that is implemented by the system of  FIG. 1  to perform fault detection. 
           [0011]      FIG. 3A  is a circuit diagram that describes operation of a solar cell in one operating mode. 
           [0012]      FIG. 3B  is a circuit diagram that describes operation of a solar cell in another operating mode. 
           [0013]      FIG. 4A  is a circuit diagram for a power converter that is configured to switch between a plurality of states. 
           [0014]      FIG. 4B  is a circuit diagram that describes the operation of the power converter of  FIG. 4A  in a first state. 
           [0015]      FIG. 4C  is a circuit diagram that describes the operation of the power converter of  FIG. 4A  in a second state. 
           [0016]      FIG. 4D  is a circuit diagram that describes the operation of the power converter of  FIG. 4A  in a third state. 
       
    
    
     DETAILED DESCRIPTION 
       [0017]    For a general understanding of the environment for the system and method disclosed herein as well as the details for the system and method, reference is made to the drawings. In the drawings, like reference numerals have been used throughout to designate like elements. As used herein, the term “operational parameter” refers to a physical property of a circuit or component in a solar power system that can be measured while the component operates. For example, a series resistance value in an individual solar cell is an operational parameter of the solar cell. Various components in the solar power system have one or more operational parameters that may be monitored during operation. As used herein, the term “operating mode” refers to a set of operating characteristics that apply to a component in a solar power system based on the conditions of the component. For example, during a normal operating mode a solar cell generates electrical voltage and current with known parameters when exposed to sunlight. The characteristics of the solar cell in the normal operating mode are modeled to enable an estimate of an output of the solar cell, such as an output voltage or current, when one or more of the operational parameters of the solar cell are measured. One or more failure modes for the solar cell include operational modes where the operational parameters and corresponding output of the solar deviate from the expected output in the normal operating mode. Systems and methods that identify changes in the operational modes of components in a solar power system, particularly failure modes, are described in more detail below. 
         [0018]      FIG. 1  depicts a solar power generation system  100 . The system  100  includes a solar panel  104  that includes a plurality of solar cells, such as solar cell  108 , a power converter  112 , and controller  120 . A solar cell monitoring device  124  is operatively connected to the solar cell  108  and the controller  120 . A power converter monitoring device is operatively connected to the power converter  112  and the controller  120 . The solar cells  108  in the panel  104  generate direct current (DC) electricity in response to light shining on the solar cells. The solar cells  108  of the panel  104  are electrically connected together to output the DC current to the power converter  112 . An electrical power output of the power converter  112  is electrically connected to a load  116 . Typical embodiments of the power converter  112  include inverters that generate an alternating current from the direct current generated by the panel  104 . Other embodiments of the power converter  112  include DC to DC power boosters. Typical embodiments of the load  116  include batteries, electrical appliances, and the electric grid. In one configuration the power converter  112  is a separate device from the panel  104 , while some solar panel embodiments include one or more power converters that are integrated with the panel. While  FIG. 1  depicts a single solar panel  104 , alternative configurations include multiple solar panels that provide electrical current to one or more power converters. The fault detection methods described herein are suitable for use with the various configurations of solar cells, panels, and power converters. 
         [0019]    Cell monitor  124  is configured to measure at least one operational parameter of a single solar cell  108  as well as an output of the solar cell  108  and provide data corresponding to the measurement to the controller  120 .  FIG. 3A  and  FIG. 3B  depict circuit diagrams that model operation of a solar cell in two different operating modes. Both models of faulty power converter include a current source  304  and diode  308  that are connected in parallel with a shunt resistance R sh    313  and in series with a series resistance R s    316 .  FIG. 3B  additionally includes a voltage controlled current source  320  that can model an avalanche effect that occurs due to the solar cell being placed in a shadow. Both models for the solar cells in  FIG. 3A  and  FIG. 3B  depict an output current I  324  and output voltage V  328 . 
         [0020]    Examples of operational parameters in a solar cell include a series resistance R s    312 , a shunt resistance R sh , and in the case of  FIG. 3B , a voltage controller current source M(V)  320 . The cell monitor  124  is configured to measure some or all of the operational parameters during operation of the solar cell  108 . Of course, the circuit components depicted in the diagrams  3 A and  3 B are models of a solar cell, and typical solar cells do not include discrete resistors and other components that are depicted in the diagrams. The cell monitor  124  may measure resistance and current values that correspond to the various operational parameters using indirect measurement techniques that are known to the art. 
         [0021]    In  FIG. 3A , increases to the values of one or both of R sh    312  and R s    316  above predetermined resistance values indicate that the solar cell  108  is operating in a failure mode instead of a normal operating model. In  FIG. 3B , the current value of M(V)  320  can indicate a failure mode when the current source  320  generates a current that reduces or reverses the output current  324 . When the current reverses, the solar cell  108  operates as a current sink instead of a current source. The solar cell monitor  124  provides data corresponding to the operating parameters of the solar cell  108  and to the actual output of the solar cell  108  to the controller  120 . As described below, the controller  120  is configured to identify an operating mode of the solar cell  108  using multiple models that include the measured operational parameters and the measured output of the solar cell  108 . 
         [0022]    Referring again to  FIG. 1 , a converter monitor  132  is operatively connected to the power converter  112 . The converter monitor is configured to measure one or more operational parameters of the power converter  112  and one or more outputs of the power converter  112 . Power converters such as power converter  112  are typically modeled as being supplied by a voltage source. As the solar cells are current source devices, the solar cell affects the operation of power converter. Therefore, measurements of the operational parameters and output of the power converter  112  can be used to identify faults that occur in the power converter  112  and in the panel  108  that supplies the power converter. 
         [0023]    The converter monitor  132  provides data corresponding to the measured operating parameters and output of the converter  112  to the controller  120 . The embodiment of the power converter  112  depicted in  FIG. 1  is modeled as a circuit depicted in  FIG. 4A . The circuit includes a voltage source  404 , inductor  408 , transistor  424 , diode  420 , capacitor  412  and resistor  416 . A diode resistor  428  is connected in parallel with the diode  420 . During normal operations, the diode resistor operates as an open circuit to enable diode  420  to control a flow of current by switching on and off in conjunction with the transistor  424 . In a failure mode, however, the diode resistance  428  drops to a lower resistance value, effectively shorting the circuit around diode  420  and reducing the effectiveness of the power converter  112  or rendering the power converter  112  inoperable. 
         [0024]    One method for identifying various operational modes of the power converter  112  includes an averaged fault diagnosis. Switching devices in electronic power circuits result in a discrete system. The topology of the circuit changes by switching the diode and transistors between “on” and “off” states. In this regard, the model requires more details and advanced techniques for fault diagnosis. Probability density evaluation and simulation for a predefined set of faults was conducted to prove the performance of fault diagnosis in simulations. More details of the averaged fault diagnosis method are described in the attached appendix. 
         [0025]    Another method for identifying various operating modes of the power converter  112  includes modeling the power converter  112  as a switched circuit. During normal operation, the power converter  112  cycles between three circuit configurations that are depicted in  FIG. 4B-FIG .  4 D. The three circuits can be modeled as three operating modes that describe the operation of the power converter  112  during the operating cycle. 
         [0026]      FIG. 4B ,  FIG. 4C , and  FIG. 4D  depict three circuit configurations that depict the operating modes for the circuit depicted in  FIG. 4A  during normal operation.  FIG. 4B  depicts the circuit of  FIG. 4A  with diode  420  in an open state and transistor  424  in a closed state.  FIG. 4C  depicts the circuit of  FIG. 4A  with both the diode  420  and transistor  424  in a closed state.  FIG. 4D  depicts the circuit of  FIG. 4A  with both the diode  420  and transistor  424  in an open state. During normal operation, the power converter  112  cycles in order between the states of  FIG. 4B-4D  at a predetermined frequency. The power converter monitor  132  is configured to identify the operational parameters of one or more components in the power converter, including the resistances of the transistor  424  and diode  420 , during operation of the power converter and to provide data corresponding to the operational parameters to the controller  120 . The power converter monitor  132  is also configured to measure an output current and voltage from the power converter and provide data corresponding to the measured output to the controller  120 . 
         [0027]    Referring again to  FIG. 1 , the controller  120  is configured to receive data from one or both of the cell monitor  124  and converter monitor  132  and to identify operating modes of the solar cells  108  and power converter  112 . Controller  120  may be implemented with general or specialized programmable processors that execute programmed instructions. The instructions and data required to perform the programmed functions may be stored in memory associated with the processors or controllers. These components may be provided on a printed circuit card or provided as a circuit in an application specific integrated circuit (ASIC). Each of the circuits may be implemented with a separate processor or multiple circuits may be implemented on the same processor. Alternatively, the circuits may be implemented with discrete components or circuits provided in VLSI circuits. Also, the circuits described herein may be implemented with a combination of processors, ASICs, discrete components, or VLSI circuits. Controller  120  is operatively connected to a memory  122 . The memory  122  stores program instructions for execution by the controller  120 . The memory  122  also stores data corresponding to previously identified operational parameters, operating modes, and outputs of the solar cells  108  and power converter  112 . 
         [0028]    The controller  120  is configured to identify the operating modes of the solar cells  108  and power converter  112  using a multiple-model adaptive estimator (MMAE).  FIG. 2  depicts an exemplary MMAE system  200 . In general, an MMAE system compares predicted outputs from two or more models to an actual measured output of a system for a given input. In  FIG. 2 , the input U(k) is supplied to a plurality of models  208 A- 208 N. Each of the models  208 A- 208 N represents a model of a system, such as the solar cell  108  or power converter  112 , in a particular operating mode. The output of the actual system  204   y ( k ), such as an electrical output signal, is also measured. The estimated output of each of the models  208 A- 208 N is compared to the actual output producing residual signals. The residual signals refer to differences between an observed output value from the actual system  204  and the estimated outputs from the models  208 A- 208 N. Thus, residual signals with zero magnitude indicate that an output of the actual system  204  matches an estimated output of a corresponding model. 
         [0029]    In the MMAE system  200 , the hypothesis center  216  weights the outputs of each of the models  208 A- 208 N based on the current residual signal identified for the model, and also with reference to a prior history of residual signal differences between the actual output from the system  204  and the estimated output from each of the models  208 A- 208 N. The prior history of residual errors used in the hypothesis center  216  enables the MMAE system  200  to weight the values of models based not only on the currently measured residual signal values, but on previous residual signals. In one exemplary configuration, model  208 B has a current residual signal value of zero, but has a history of residual values with large magnitudes, while model  208 A has a non-zero current residual signal value with a history of low or zero magnitude residual signal values. The hypothesis center  216  weights the output of model  208 A more heavily even though the current residual signal value for the model  208 A is greater than the residual signal value for model  208 B based on the historic residual signal values for both models. 
         [0030]    MMAE systems, such as system  200 , often include various filters, including Kalman filters, to compensate for noise in the input U(k) and in the corresponding outputs from the models  208 A- 208 N and from the system  204 . The exemplary MMAE system  200  additionally includes self-tuning modules  212 A- 212 N. Each of the self-tuning modules  212 A- 212 N is configured to adjust a corresponding one of the models  208 A- 208 N to account for changes in the operating parameters in each model that may occur over time. Examples of changes in an operating parameter for a model that occur over time include changes to internal resistance of a solar cell, or changes to the switching characteristics of the power converter. Thus, the self-tuning modules  212 A- 212 N are configured to selectively discount or “forget” prior residual signal values when the operating parameters of a selected model change over time. The self-tuning modules  212 A- 212 N enable the MMAE system  200  to compensate for changes in the operating parameters of the actual system  204  in each of the models  208 A- 208 N. The tuning modules  212 A- 212 N may employ various algorithms, including the forgetting-factor recursive least square (FFRLS) algorithm. The hypothesis center  216  selectively discounts the weight of historic residual signal values from each of the models  208 A- 208 N based on the tuning values generated by each of the self-tuning modules  212 A- 212 N. 
         [0031]    The hypothesis center  216  generates a plurality of probability values that are assigned to each of the models  208 A- 208 N. Each probability value indicates a probability that the actual system  204  is presently operating in an operating mode that corresponds to each one of the models  208 A- 208 N. The MMAE system  200  generates a probability distribution  220  with probability values assigned to each of the models  208 A- 208 N. In one configuration, the controller  120  identifies the model having the highest probability value in the distribution  220  as the current operating mode of the actual system  204 . As described in more detail in the attached appendix, the MMAE system  200  is configured to identify changes in the operating mode of the solar cells  108  and power converter  112  in the system  100  using a small number of data samples. Thus, the controller  120  is configured to identify and take appropriate action in a short time period when transient faults occur. 
         [0032]    In operation, the solar cells  108  in the solar panel  104  generate electricity that is supplied to the power converter  112  and subsequently to the load  116 . The controller  120  receives operating data and output data from the cell monitor  108  and applies the data to an MMAE system. Controller  120  employs circuit models, such as the circuit models depicted in  FIG. 3A  and  FIG. 3B  as models in the MMAE system. In situations where the controller  120  identifies that the operating mode of the solar cell  108  and the power converter combined corresponds to a failure mode, the controller  120  is configured to open a panel switch  110  that electrically isolates the panel  104  from the power converter  112 . Since many fault conditions are transient in nature and last only for a short period of time, the controller  120  is also configured to close the switch  110  when the solar cell  108  returns to a normal operating mode. In an alternative configuration, each of the cells  108  may be individually coupled to an electrical switch that isolates each cell from the remaining cells in the solar panel  104 . 
         [0033]    The controller  120  is also configured to monitor the operating modes of the power converter  112 . As seen in  FIG. 4B-FIG .  4 D, the power converter cycles between three different operating modes during normal operation. The controller  120  includes an MMAE system that incorporates the three normal operating modes as well as one or more fault modes, such as when the diode  420  disrupts the output of the converter. The controller monitors the output of the MMAE system to identify the expected cyclical changes in operating mode between the three normal operating modes for the power converter  112 . The controller identifies a fault when the identified operating mode is not one of the three normal operating modes, or when the identified operating modes do not cycle with the expected operating frequency of the power converter  112 . The controller  120  is configured to open the switch  114  to electrically isolate the power converter  112  from the load  116  in response to detecting the fault. 
         [0034]    While  FIG. 1  depicts controller  120  as being configured to monitor both solar cells  108  and the power converter  112 , alternative configurations of the solar power generation system  100  connect the controller to either the solar cells  108  or the power converter  112 . Additionally, alternative monitoring systems can monitor an entire panel, such as panel  104 , instead of monitoring a single solar cell. The circuit models presented above are exemplary of models that are suitable for use with the solar cell and power converter embodiments described herein. Alternative components and configurations used in solar power generation systems include different circuit models that correspond to normal operating modes and failure modes associated with each alternative configuration. 
         [0035]    In various configurations, the controller  120  performs actions in addition to or instead of operating the switches  110  and  114  when a fault operating mode is identified. In one configuration, the controller  120  generates a record of the failure, including information, such as the time and duration of the failure, and stores the record in the memory  122 . Some embodiments of the controller  120  include a networking module (not depicted) that transmits alerts or records of faults via wired or wireless data networks to a remote computing device for further monitoring and diagnostics. 
         [0036]    While the embodiments have been illustrated and described in detail in the drawings and foregoing description, the same should be considered as illustrative and not restrictive in character. It is understood that only the preferred embodiments have been presented and that all changes, modifications and further applications that come within the spirit of the invention are desired to be protected.
A method for detecting faults in a solar cell includes measuring at least one operational parameter of a solar cell, measuring an output of the solar cell, identifying differences between the measured output of the solar cell and estimated outputs of a first and second model of operating modes of the solar cell, generating probabilities corresponding to the likelihood that each model corresponds to the actual operating mode of the solar call based on the identified differences, and disconnecting the output of the solar cell from a load in response to the identified current operating mode being the operating mode of the second model.