Patent Application
20110066401
This invention is related to energy converters. More particularly, this invention is related to monitoring the performance and diagnosing any underperformance of energy converters, such as photovoltaic arrays.
Because they rely on a freely available and renewable energy sources, are environmentally friendly, and pay for themselves by reducing energy costs, photovoltaic (PV) modules are used on an increasing number of homes and businesses. When PV modules are combined in a PV power plant, they can power entire communities. When these PV modules in a PV power plant are not operating at optimum efficiency, however, their underperformance is felt on a larger scale: Entire communities can be affected by lower power production. By some estimates, underperforming modules in PV power plants reduce productivity and resulting profits of the PV power plant operators by up to 20%.
Some PV power plants are monitored using “Symmetry Analysis,” a method that compares the currents through different strings of a PV module. When any two currents differ by a predetermined amount, the monitoring system determines that the string with the smaller current is underperforming and generates an alarm message. Other monitoring systems use a “day-before” comparison, in which the day's current through each string is compared to the previous day's current through the same string. Large enough differences again indicate a malfunctioning string.
Whatever abnormality-discovering method is used, staff are required to monitor the performance of the PV modules around the clock. This type of monitoring is only as effective as the staff are diligent and the measuring equipment is accurate. Even then, most staff members are not trained to determine whether any underperformance is truly indicative of a malfunctioning PV module and, if so, the cause. Even fewer staff are qualified to determine how to remedy the underperformance. By the time a problem is found and a remedy is applied, the accumulated lost productivity can be significant.
In accordance with embodiments of the invention, a system monitors one or more energy converters, such as a photovoltaic array or wind turbine, to ensure that they are operating at acceptable levels. The system compares the actual output of the energy converter to a predicted output, generated using a mathematical model of the energy conversion unit. When the system determines that the energy converter is underperforming, it determines possible reasons for the underperformance, schemes to diagnose the underperformance, and remedial actions for increasing the performance to acceptable levels. All of this information can be displayed to personnel monitoring the output generated by energy converters. This information, or a subset of it, is then assembled into messages transmitted to personnel to service the energy converters.
In one aspect, a system for monitoring an efficiency or health status of an energy converter includes a module that determines an amount an output of the energy converter differs from a predicted output (an underperformance value), a possible cause of underperformance, a strategy for diagnosing the possible cause of the underperformance, a corresponding remedial action, or any combination thereof. The predicted output is based on operating conditions of the energy converter, such as a current time of day, a current month, or both. Alternatively, the operating conditions correspond to a microclimate surrounding the energy converter.
The system also includes a monitor for measuring the output of the energy converter and a transmission module for notifying an agent (e.g., a staff member or dedicated service personnel) when an underperformance metric of the energy converter exceeds a predetermined threshold.
The predicted output, the possible cause of the underperformance, a strategy for diagnosing the possible cause of underperformance, the corresponding remedial action, or any combination thereof are automatically determined using a learning algorithm.
In a second aspect, a system for monitoring an efficiency of a photovoltaic array includes a monitor that measures an output of the photovoltaic array and a first module that determines an amount the output differs from a predicted output of the photovoltaic array. The first module also determines a possible cause of underperformance for the photovoltaic array, a strategy for diagnosing the possible cause of underperformance, a corresponding remedial action, or any combination thereof.
Possible causes of underperformance include theft or vandalism of a component of the photovoltaic array, a fault in the photovoltaic array, a presence of an object blocking illumination to the photovoltaic array, or any combination thereof.
In one embodiment, the predicted output is determined from an amount of irradiation striking the photovoltaic array, an incidence angle of irradiation striking the photovoltaic array, a temperature of the photovoltaic array, or any combination thereof. Alternatively, or additionally, the predicated output is based on predetermined operating characteristics of the photovoltaic array.
In a third aspect, a system includes a first module that determines underperformance values of multiple energy conversion units in multiple different geographic locations and a second module that determines for each of the underperformance values a possible cause of underperformance, a strategy for diagnosing a cause of the underperformance, and a corresponding remedial action. The first module monitors outputs from each of the multiple energy conversion units to determine the underperformance values. The second module monitors current microclimates surrounding each of the multiple energy conversion units to determine the underperformance values.
In a fourth aspect, a device includes multiple light detectors aimed in different directions. The device is configured to determine irradiance impinging on the multiple light detectors. A portion of the multiple light detectors are directed outwardly at different angles about a central axis. The different directions include a first direction along a first vector and second directions at one or more angles to the first vector.
In one embodiment, the multiple light detectors include a pyranometer directed in the first direction and multiple photosensors directed in the second directions. Preferably, the device also includes an opaque shield between the pyranometer and the multiple photosensors. The shield is arranged, in size and location, to shadow the multiple light detectors from sunlight traversing an arc through a normal to the pyranometer.
In accordance with embodiments of the invention, energy converters, such as photovoltaic (PV) cells, wind turbines, and water turbines, are monitored in real time to ensure that they are performing at acceptable, pre-determined levels. Performance metrics, such as power generated for the day, are displayed on a Web or other page. When an energy converter underperforms, the amount of underperformance is automatically calculated and used to determine the cause of underperformance and possible remedial actions. The remedial actions are included in instructions to service personnel, who can then service the energy converter. By taking steps to quickly return the energy converter to acceptable levels of operation, the overall output of the energy converter is maximized, critical in large-scale energy conversion systems such as PV power plants. In this way, the overall health status of an energy converter (or multiple energy converters) can be monitored and maintained.
Underperformance can be determined in any number of ways. As one example, underperformance is measured as the difference between a predicted power output of the energy converter and its actual power output. When the energy converter is a PV module composed of multiple PV arrays (solar panels), the output is predicted by generating a mathematical model that characterizes an optimal output based on parameters such as solar radiation, temperature, time of day, orientation of the PV module to the sun, and PV module ratings, to name only a few such parameters. The actual output, whether measured in power, current, or voltage, is compared to this benchmark to determine an underperformance metric. The model can be refined over time to increase its accuracy.
It will be appreciated that the displays 155 and 170 can be on any type of device. As only some examples, the display 155 is on a personal computer, a smart phone, or a personal digital assistant, and the display 170 is on a smart phone or a pager capable of displaying short message service (SMS) messages.
Many conditions can cause the PV module 100 to underperform, with corresponding different remedial actions.
The “alert” message 210 in
The possible causes of underperformance and corresponding remedial actions can be determined from a number of factors, including the amount of underperformance, the rate of change of underperformance, current weather conditions, and the current season, to name only a few factors. For example, quick but large changes in performance during calm summer months can indicate a component failure, requiring the replacement of the component. Small but quick changes during windy months can indicate the falling of a branch or leaves onto a surface of the array of PV cells, requiring service personnel to bring a pole grabber. Small but gradual changes during dry months can indicate the accumulation of dust or soot on a panel of a PV module, requiring service personnel to bring a spray washer. Small but gradual changes during cloudy moments followed by a return to normal performance levels can indicate the movement of overhanging clouds, requiring no action by service personnel.
The CDAQ 113 receives sensor signals from the SCM 111 and transforms the signals into computer-understandable words that are manipulated, compared, analyzed, and stored in database tables for future retrieval, processing, and display. The CDAQ 113 also transmits performance and other information, such as over the Internet, through the Internet Gateway module 119, for display on a Web page.
Normally, the CDAQ 113 samples performance metrics at a rate of about 3 samples per second. In a hardware diagnostic mode, during which the CDAQ 113 performs burst sampling at a rate of up to 100,000 samples per second, the CDAQ 113 is also referred to as a high-speed digital signal processor because it brings signals from points at the interface between the PV cells and the inverter 130 (
The TVD 115 is used to detect the theft or vandalism of components of the PV module 100. The TVD 115 functions by monitoring “always-on” electrical signals, generated when the components of the PV module 100 are in place and working properly. When any component of the PV module 100 is disconnected or vandalized, a corresponding “always-on” electrical signal is turned off. This condition is transmitted to a database server and supervisory program for validation. An alert message is then generated and transmitted to designated personnel.
The CFD 117 monitors the PV module 100 for specific malfunctioning components, such as a leaky capacitor, shorted or open diodes, or other physical damage to the PV module 100 or inverter 130. This condition is transmitted to the database server and the supervisory program for validation, and an appropriate alert message is transmitted to the designated personnel.
The module 110 includes computer-readable media containing the algorithms for performing the steps executed by the SCM 111, the CDAQ 113, the TVD 115, the CFD 117, and the Internet Gateway module 119. The module 110 also includes one or more processors configured to execute those steps. The number of instances of each of the modules 111, 113, 115, and 117 depends on the size of the entire energy converter. Each of the modules 111 and 113 is capable of accommodating up to 8 sensor signals, while each of the modules 115 and 17 works with one string of solar panels.
In accordance with embodiments of the invention, a data warehouse stores, among other things, performance and service data used to track underperformance and to determine remedial actions. In one embodiment, a data warehouse is remote from the module 110 (
Referring to
The APM 420 expresses the conditional relationship between various underperformance conditions and an array of possible fault sources. The APM 420 can be accessed and manipulated by algorithms to sort out the most likely faulty conditions from among a list of candidates to be selected and reported. The APM 420 is also affected by the “personality” characteristics as captured by “PV site attribute database elements,” such as shown in
The KBS 430 contains entries that correlate causes of underperformance with remedial actions. The KBS 430 stores underperformance information, remedial actions, and related information in a “knowledge representation” format that allows for manipulation by processing algorithms. As one example, the KBS 430 stores data, rules that indicate knowledge, and deduction rules, all manipulated by an induction engine that correlates symptoms, underperformance metrics, and remedial actions. The correlations can be updated and fine-tuned using learning algorithms. For example, it may be determined that a cause of underperformance is more likely than previously thought; the KBS is 430 is updated to reflect this. The underperformance values and corresponding remedial actions can later be stored in different formats, such as in a relational database, that allows for easy storage, retrieval, and display.
The DWD 400 also includes three software programs, a Knowledge Acquisition Module (KAM) 440, a Knowledge Discovery Module (KDM) 450, and a Fault Model Programming (FMP) module 460. The KAM 440 and the KDM 450 function with the KBS 430 to acquire or discover new elements so as to increase or refine the knowledge as it relates to performance issues and characteristics for fault conditions in a solar power plant. The FMP module 460, uses algorithms to develop one or more mathematical models used to receive any combination of measures of underperformance, current weather conditions, and operating characteristics of the PV module 110 and, from them, determine possible causes of underperformance and corresponding remedial actions. The FMP module 460 cooperates with the KBS 430 to account for conditions and to grow and evolve knowledge or algorithms for diagnosing faults. Using heuristics or other learning algorithms, these mathematical models are refined to more accurately predict the possible causes of underperformance, the remedial actions, or both.
The data in the DWD 400 are accessed by a Fault-Diagnostic Inference Engine (FIE), a supervisory program that takes symptoms of underperformance and returns possible causes of underperformance, corresponding remedial actions, or both.
It will be appreciated that the elements described above are only illustrative of one embodiment and that any of the elements can be replaced with a similarly functioning element. For example, the FDx 410 can be replaced with any element that lists faulty components or external factors with attendant symptoms associated with each condition or failure. Similarly, the APM 420 can be replaced with any element that captures conditional probabilities associated with a given symptom of various faulty conditions due to internal failure or external conditions or factors, as accumulated from operational experience, or electrical or physical relationships.
It will also be appreciated that in accordance with embodiments, performance and other data can be collected independently of their analysis. Thus, for example, data can be collected periodically but analyzed in response to specific commands, for particular purposes. When current data is needed, for whatever purpose, a new data collection process can be initiated independently of, and thus without disturbing, any ongoing data processing. The data collection and data analysis components can thus be modular, operating independently of each other.
Referring to
In one embodiment, sun information is determined from an insolation map such as the insolation map 600 in
While
Referring to
The entries in table 650 can be input in any number of ways, such as by an operator with statistics about causes of underperformance. Later, the entries can be updated automatically by learning algorithms known to those skilled in the art. The entries, or information corresponding to them, can be stored in a knowledge based system (KBS), such as KBS 430 in
The FDx 700 of
Still referring to
The term “underperformance” can refer to any value that reflects a level of operating inefficiency of a PV module. For example, the term can refer to a percentage that the actual (e.g., measured) power (PA) differs from the predicted power. The term can refer to the difference (ΔP), measured in Watts, between an optimal power output (Popt) for a PV unit and PA. The term can refer to a normalized value, such as 1−(Popt−PA)/Popt. Those skilled in the art will recognize other values that can be used to measure the operating efficiency or inefficiency of a PV unit.
As used herein, “performance” can be refer to a measure of voltage, current, or power output from a PV module. Those skilled in the art will recognize other measurable parameters that can be used to indicate the performance of a PV module.
In accordance with different embodiments, one or more mathematical models are derived to determine what is variously referred to as an “optimal,” “predicted,” or “benchmark” performance value, such as power output (e.g., Popt, discussed above).
Applying the equivalent circuit theory by Thevenin and Norton, every PV array can be represented by an equivalent circuit for optimally operating the array, nominally derived from a datasheet of every brand and model of PV modules—namely open circuit voltage, short circuit current, maximum power voltage, and maximum power current, all by applying the series and parallel configuration of a PV array. Thus, an equivalent circuit of a well-functioning PV array can be characterized by a region in an IV-Chart, driven continuously by its environmental conditions, but nevertheless quantified mathematically. This dynamically changing region can be referred to as the “sweet spot” for a PV array or power plant. A set of entries in a family of database tables will fully characterize the generic, as well as unique, aspects of a PV site.
Equation (1) below is a mathematical model derived using characteristics for predicting the performance in accordance with one embodiment of the invention, used to determine “underperformance.” When PA varies significantly from the mathematically computed “sweet spot” in Equation (1), the system is considered underperforming.
P
opt
=S*Cos(Φ)*D*Area(1−(K*(T−25))) Equation (1)
The values S, Φ, and T can be measured in any number of ways. As one example, S is measured by a pyranometer mounted alongside the array of PV cells on top of a roof, Φ is determined by the time of day and current month, and T is measured by a thermocouple mounted alongside the array of PV cells. D is a rating, determined for each array of PV cells identified by manufacturer and part number.
Equation (1) estimates Popt by sensing the direct normal component of sunlight. It has been determined that diffused components of sunlight also strike the surface of PV cells. This is especially pronounced on cloudy days, when a larger percentage of light striking a PV array is reflected or diffused light. In accordance with embodiments of the invention, an irradiance sensor is arranged to sense not only the direct normal component of sunlight but also directional diffused components, thereby more accurately detecting more of the energy striking the PV cells and thus more accurately predicting Popt for the solar array.
The pyranometer 751 and photosensors 760-767 all generate signals corresponding to the irradiance striking them. These signals are transmitted along the cables 751A and 760A-767A coupling the pyranometer 751 and photosensors 760-767, respectively, to a processing module (not shown) that translates the signals into a combined irradiance metric for measuring a performance of a PV array.
Referring to the x-y-z coordinate system shown in
When the z-axis is directed to the sun at its azimuth and the x-y plane is aligned with the horizontal, the angle that a particular photosensor 760-767 makes with the horizontal is referred to as the “elevation” or “tilt” angle. (This angle equals 90−Φ.)
It will be appreciated that the x-y-z coordinate system is shown only for explanation. Other reference systems, oriented in different ways, can also be used to describe the embodiments.
The angular rotation about the z-axis for a particular photosensor 760-767, relative to a reference point, is referred to as the “pan angle” (Ω). Together, the tilt and pan angles define a direction.
Preferably, each of the photosensors 760-767 has operational characteristics similar to those of the junction materials in the PV array whose performance is being monitored. In this way, the photosensors 760-767 mimic and thus more accurately track the performance of the PV array. In one embodiment, the photosensors 760-767 are mono crystalline silicon sensors, though other types of sensors can also be used.
It will be appreciated that the photosensors 760-767 can be arranged in any number of ways to capture sunlight reflected from different directions. Furthermore, while the funnel mount 770 has a frusto-conical shape, it will be appreciated that mounts with other shapes configured to direct or aim the photosensors 760-767 outwardly, at different directions, can also be used. In other embodiments, at least some of the photosensors 760-767 are spaced non-uniformly along the outer surface of the funnel mount 770.
In accordance with one embodiment, Popt calculated for a PV array using the irradiance sensor 750 is determined by Equation (2):
P
opt=IrrEff*Cos(Φ)*D*Area*(1−K*(Tc−25))*FaultSources Equation (2)
It has been determined that the accuracy of irradiance measurements is increased by substantially limiting one set of light sensors to measure direct normal sunlight and another set to measure indirect, diffused light. In accordance with one embodiment,
It will be appreciated that the light shield 785 can have different configurations and still achieve the principles of the invention. In the embodiment of
Preferably, the light shield 785 includes an opaque material. Also preferably, the light shield 785 is constructed to withstand temperature extremes, precipitation, wind, and other outdoor conditions. As one example, the light shield 785 comprises stainless steel with an anti-reflective coating. Those skilled in the art will recognize other suitable materials for the light shield 785.
The light shield 785 can be temporarily removed for calibration or during troubleshooting or maintenance operations.
In different embodiments, the irradiance sensor 750 or the irradiance sensor 790 replaces the irradiance sensor 145 shown in
The irradiance sensors 750 and 790 leverage the power of embedded computing and intelligent server resources to capture direct and diffused energies from the sun. Preferably, the irradiance sensors 750 and 790 contain no moving parts and thus are low-cost approaches for sensing light energy.
Every PV power plant site is uniquely defined by a set of characteristics such as location-latitude and longitude, mounting of the individual PV modules, brand and model of the PV modules, and micro-climate of the site, to name only a few characteristics. This “personality,” sometimes characterized qualitatively, other times quantitatively, is used to determine any operating abnormalities.
The entries in table 800 are all taken into account when modeling Equation (1).
The benchmark output in power, voltage, or current (and thus Equations (1) and (2) above) is based on different parameters, such as the materials from which the PV module is made, the test conditions used to rate the performance of the PV module, and other factors, all of which are discussed below.
Among other things, the performance of a PV module depends on the module material. The conversion efficiency of amorphous silicon modules varies from 6% to 8%. Modules of multi-crystalline silicon modules have a conversion efficiency of about 15%. Mono-crystalline silicon modules are the most efficient, with a conversion efficiency of about 16% to 24%. Modules are roughly 1 m2 to 1.5 m2 in area, and getting larger, and typically include between 36 and 72 individual PV cells.
The DC output of solar modules is rated by manufacturers under Standard Test Conditions (STC). These conditions are easily recreated in a name-plate and allow for consistent comparisons of products, but they must be modified to estimate output under common ambient operating conditions. STC conditions include a solar module temperature of 25° C.; a solar irradiance (intensity) of 1,000 W/m2 (often referred to as peak sunlight intensity, comparable to clear summer noon-time intensity); and a solar spectrum as filtered by passing through 1.5 times normal of atmosphere (ASTM Standard Spectrum). A manufacturer can rate a particular solar module output at 200 Watts of power under STC and call the product a “200-watt solar module.” This module will often have a production tolerance of +/−5% of the rating, which means that the module can produce 190 Watts and still be called a “200-watt module.”
The electrical current generated by photovoltaic devices is also influenced by the spectral distribution (spectrum) of sunlight. It is also commonly understood that the spectral distribution of sunlight varies during the day, being “redder” at sunrise and sunset and “bluer” at noon. The magnitude of the influence that the changing spectrum has on performance can vary significantly, depending on the PV technology being considered. In any case, spectral variation introduces a systematic influence on performance that varies by time-of-day. Similarly, the optical characteristics of PV modules or pyranometers can result in a systematic influence on their performance related to the solar incidence angle.
Since roughly 80% of the sun's energy is dissipated into heat, PV module output power reduces as the module temperature increases. When operating on a roof, a solar module will heat up substantially, reaching inner temperatures of 50° C. to 75° C. For crystalline modules, a typical temperature reduction factor recommended by the California Energy Commission is 89% or 0.89. Therefore, the 200-Watt solar module will typically operate at about 170 Watts (190 Watts*0.89=170 Watts) in the middle of a spring or fall day, under full sunlight conditions. To ensure that PV modules do not overheat, they must be mounted in such a way as to allow air to move freely around them. This is particularly important in locations that are prone to extremely hot midday temperatures. The ideal PV module operating conditions are cold, bright, sunny days.
Dust or soot can accumulate on the PV module surface, blocking some of the sunlight and thus degrading output. Although typical dust is washed away during rainy seasons, it is more practical to estimate system output taking into account the reduction due to dust buildup in the dry season. A typical annual dust reduction factor is approximately 5% or 0.95. Therefore, a 200-Watt solar module operating with some accumulated dust may operate, on average, at about 79 Watts (170 Watts*0.93/2=158 Watts/2).
A 1.6 GW STC group of grid-tied solar arrays (as specified under STC conditions) located on the Googleplex in Mountain View, Calif., U.S.A., was studied by a team at Google and publicized. As confirmed by the study, layers of dust or soot that accumulate over time may degrade the PV module's output by as much as 7%. The mathematical models of Equations (1) and (2) can thus be enhanced with an element that represents the accumulated layer of dust, with modifiers for a region's dust and rainfall characteristics, which can be tracked and modified by the occurrence of rainfall or cleaning. A nominal 0.1% degradation may be used as baseline model, for every week that goes by without any intervening event, such as rain or high winds.
The maximum power output of a total PV module is always less than the arithmetic sum of the maximum output of the individual modules. This difference is a result of inconsistencies in performance among modules, and is called “module mismatch,” which can result in roughly 2% loss in system power. Power is also lost due to resistance in the system wiring. These losses should be kept low with proper wire-sizing and good workmanship, but it is often difficult to keep them below 3%. A common derating factor for these losses is 95%.
The DC power generated by the solar module must be converted into common household AC power using an inverter. Some power is lost in the conversion process, and there are additional losses in the wires from the rooftop array, down to the inverter, and out to the house panel. Modern inverters commonly used in residential PV power systems have peak efficiencies of 92% to 94%, as indicated by their manufacturers, but these again are measured under well-controlled name-plate conditions. Actual field conditions usually result in overall DC-to-AC conversion efficiencies of about 88% to 92%, with 90% or 0.90 a reasonable compromise. Thus, a 200-Watt solar module output, reduced by production tolerance, heat, dust, wiring, AC conversion, and other losses should translate into about 136 Watts of AC power delivered to the house panel during the middle of a clear day (200 Watts*0.95*0.89*0.93*0.95*0.90=134 Watts).
The PV module should be positioned and mounted to absorb the most energy from the sun. If the photovoltaic modules have a fixed position, their orientation with respect to the south (northern hemisphere), and tilt angle, with respect to the horizontal plane, should be optimized. For grid-connected PV systems in the U.S., for instance, the optimum tilt angle is about 25 degrees. For regions nearer to the equator, this tilt angle will be smaller, and for regions nearer the poles, it will be larger. The output from the array will rise gradually from 0, during dawn hours, increase with the sun angle to its peak output at solar noon, and then gradually decrease into the afternoon and back down to 0 at night. While this variation is due in part to the changing intensity of the sun, the changing incidence angle also has an effect. The pitch of the roof or tilt angle or structural frame will affect the sun angle on the PV module plane (e.g., angle θ in
Performance information, remedial actions, and other types of data measured and generated in the embodiments can be displayed in any number of ways. Messages can be transmitted for display to the building to which the PV module is mounted, to a central location used to monitor multiple PV modules at geographically dispersed locations, to a repair person making rounds, or to any other person or location. The information can be transmitted over local area networks, over the Internet, using wireless transmissions such as WiFi or cellular, to a cell phone or personal digital assistant, or by any other means.
Preferably, messages are categorized according to the amount that the output is degraded, the amount of underperformance. As one example, a message is categorized as an “alert” when system performance is 10% below the norm, as a “warning” when system performance is 20% below the norm, and as an “alarm” when system performance is 30% or more below the norm. With these categorizations, service personnel can quickly determine in what order and how quickly sites must be serviced. When the system performance is within acceptable limits, such as no more than 5% below the norm, an “OK” message, along with a relative percentage of the benchmark level, is transmitted, thereby letting operators know that the notification system is functioning. Of course, other thresholds based on other percentages of degraded output can also be used.
In one embodiment, one or more Web pages or other electronic textual elements display various parameters used to track the performance of a PV site such as:
Preferably, a user is presented with information that allows him to track the output of a PV module or PV power plant and understand why the PV module or PV power plant is not performing as expected.
In some embodiments, customers can subscribe to Web services offered in accordance with the embodiments. With this service, a central site operator monitors PV arrays at a customer site and provides the customer with one-time or periodic reports detailing the performance of the PV arrays. The customer can select the type of performance data included in the reports.
In some instances, the actual irradiance striking a PV array cannot be determined. For example, a location is too remote for personnel to install an irradiance sensor adjacent to PV arrays. In accordance with one embodiment, a mathematical model (e.g., Equations (1) and (2), above) is generated using parameters other than the irradiance, such as air temperature or other environmental data surrounding or sufficiently close to the location being monitored. The location is thus “virtually” visited. In one embodiment, agent-like programs are dispatched to harvest environmental data surrounding an area and used to approximate modeling parameters.
In the step 1013, the process accesses Table 650 in
In alternative embodiments, the step 1017 is supplemented with the step of automatically taking the remedial action. As one example, when the remedial action is spray washing a surface of the PV module, this action is taken automatically by triggering a rooftop sprinkler system to wash away leaves or other debris. Those skilled in the art will recognize other remedial actions that can be taken automatically.
It will be appreciated that the steps 1000 of
It will be appreciated that references to “cause of underperformance” and “remedial action” can refer to single or multiple causes and remedial actions. Each is referred to in the singular merely to simplify the discussion.
In one embodiment, the steps 1000 are performed by a processor executing instructions on a computer-readable medium. In different embodiments, the computer-readable medium is programmed using software, hardware, firmware, any other means for executing instructions, or any combination of these. It will be appreciated that the functionality shown in
While the examples discussed above illustrate monitoring a single PV array at a single location, it will be appreciated that multiple PV modules at different geographic locations can be monitored at a one or more central locations.
The Web page display unit 1101 shows information for the PV modules 1-3, respectively, similar to that includes in the Web page 900 in
While the examples above describe PV modules, it will be appreciated that the principles of the invention are suitable for monitoring the output of other types of energy conversion units.
In accordance with embodiments of the invention, multiple PV modules are mounted to rooftops at sites at different geographic locations. For each PV array, information is stored at a central location, information such as location (e.g., latitude and longitude), operating specifications for the PV module, and orientation relative to the sun's azimuth angle for the location. A mathematical model used to predict performance (e.g., power output) for each PV module is generated. The predicted performance is based on the current time and the current weather conditions surrounding the PV module, including the intensity of the sun, cloud cover, wind speed, and the like. Preferably, the predicted performance is modeled, for example, using Equation (1) or Equation (2) above. The central location also houses a database populated with possible causes of underperformance, diagnostic methods, and remedial actions.
In operation, the output of each PV module is periodically measured and transmitted to the central location. At the central location, performance information for each PV array is displayed to monitoring personnel. The measured performance is compared to the predicted performance, and if the difference between the two is above a threshold value, the system determines that the PV module is underperforming.
The amount of underperformance is used, with other variables such as the current weather and output history of the PV module, to determine diagnostic strategies and remedial actions. The diagnostic strategies and remedial actions are explained in messages transmitted to service personnel who are dispatched to service the underperforming PV arrays. In this way, the output of the PV arrays can be maintained at optimal levels; preferably, any diminished output levels are restored.
In one embodiment, the predicted performance is based on irradiance measured at each site. The irradiance is determined by an irradiance sensor having a pyranometer directed to the sun at its azimuth and multiple photosensors directed at various angles relative to the pyranometer. An opaque light shield is located between the pyranometer and the multiple photosensors.
After service personnel have visited sites, they input data indicating the actual causes of underperformance, strategies they used to determine the actual causes of underperformance, and the actual diagnosed cause of underperformance. This updated data is used by learning systems and other artificial intelligence components to update and refine the mathematical models (e.g., the coefficients of the mathematical models) and the databases that correlate the underperformance metrics, the causes of underperformance, the diagnostic strategies, and the remedial actions.
After reading this application, those skilled in the art will recognize many possible variations within the spirit of the invention. For example, a table of the causes and remedial actions can be stored on a device carried by service personnel. In this way, rather than transmitting text describing the causes of underperformance and corresponding remedial actions to service personnel, only the indices corresponding to the table entries need to be transmitted. It will be readily apparent to one skilled in the art that other modifications may be made to the embodiments without departing from the spirit and scope of the invention as defined by the appended claims.
This application claims priority under 35 U.S.C. §119(e) from the co-pending U.S. provisional patent application Ser. No. 61/241,523, filed Sep. 11, 2009, and titled “Diagnostic System for a Photovoltaic (Renewable) Power Plant,” which is hereby incorporated by reference.
| Number | Date | Country | |
|---|---|---|---|
| 61241523 | Sep 2009 | US |
