6 Ways to Read Monitoring Data for Early Detection of Power Generation Declines

When managing a solar power generation system, there are days when you may feel that "generation is low." However, generation fluctuates due to multiple factors such as weather, solar irradiance, temperature, season, shading, soiling, equipment condition, and grid-side control. Therefore, judging a fault based on the generation of a single day risks mistaking natural variation for an abnormality. On the other hand, overlooking a clear decrease in generation can lead to reduced feed-in and self-consumption, delays in inspection response, and prolonged identification of the cause.

The key to early detection is not to view monitoring data solely in terms of whether power generation is high or low. By combining the relationship with solar irradiance, comparisons with past data, differences between sections within the installation, generation curves by time of day, records of stoppages and curtailments, and decision criteria that lead to on-site inspections, it becomes easier to detect signs of decline. This article, aimed at practitioners searching for "low power generation", organizes and explains six perspectives for early detection of declines in power generation from monitoring data.

Table of Contents

• Do not judge a decline in power output based solely on a single day's low monitoring data.

• Approach 1 Examine the deviation of power generation relative to solar irradiance

• Perspective 2: Look for a downward trend by comparing with past data

• Perspective 3: Examine differences between power conditioner units

• Viewing method 4: Look for localized decreases in string- and circuit-level data

• View 5: Identify abnormal time periods from time-of-day power generation curves

• Viewing method 6: View alert history together with on-site conditions

• Operational approach for linking monitoring data to inspection decisions

• Summary

Do not judge a drop in power generation solely by a single day's low monitoring data

When you feel that power generation is low, the first thing to check is whether the low output is really caused by equipment faults or is a natural variation due to weather and season. Because solar power generates electricity from sunlight, generation can vary greatly depending on cloudy skies, rain, snowfall, yellow dust, fog, or changes in cloud cover before and after typhoons. In summer, longer daylight hours tend to extend generation time, while higher panel temperatures can reduce output. In winter, temperatures may be advantageous, but shorter sunlight hours and a lower solar elevation make systems more susceptible to shading.

Therefore, you should avoid concluding from the monitoring screen—by looking only at daily generation—that "it's less than yesterday" or "lower than last month." To correctly assess a decrease in generation, you need to compare it with data under similar conditions. By combining generation on clear days, past data from the same season, other sections within the same plant, solar irradiance data, and generation relative to installed capacity, it becomes easier to distinguish whether the difference is simply due to weather variation or whether there is a factor causing decreased performance on the equipment side.

In practice, when a report comes in that power generation is low, it is important not to head to the site immediately but first narrow down the scope and timing of the anomaly using monitoring data. Whether the output is low overall, only for some equipment, only in the morning, only dropping at midday, has continued for several days, or occurs only on specific days will change the causes to suspect. If it is low overall, check the weather, solar irradiance, output control, and grid-side effects. If only part is low, check for panel soiling, shading, string faults, poor connections, and equipment shutdowns.

Also, for early detection of power generation declines, it is important to look not only at absolute values but also at whether the behavior deviates from the usual pattern. It is natural for daily generation to fluctuate somewhat, but if—under similar solar irradiance conditions—generation is lower than before, if only certain equipment is producing less than others, if the shape of the generation curve is distorted, or if alerts are occurring frequently, these changes are worth treating as signs of an anomaly. Monitoring data is not simply a record of generation results; it serves as material for finding early warning signs of a decline.

To detect declines in power generation early, it is essential not to let daily monitoring end as mere "inspection work" but to have standards for comparison. Without standards, judgments about whether output is low will depend on the inspector's intuition. Conversely, if you decide how to view factors such as solar irradiance, past performance, equipment-specific variability, and time-of-day curves, even inexperienced staff can more easily pick up potential anomalies. From here, we will look specifically at six perspectives to keep in mind when reviewing monitoring data.

Approach 1: Check deviations between power generation and solar irradiance

When judging a decrease in power generation, the fundamental comparison is with solar irradiance. In photovoltaic systems, less sunlight means less power generation. Therefore, you should not judge low output solely by the generation figures; you also need to check how much sunlight there was on that day. Low output on a day that is not sunny is a natural result, but if there was sufficient sunlight and output still did not increase, there may be some degradation or fault on the equipment side.

In practice, when analyzing the relationship between solar irradiance and power generation, it is helpful not only to look at daily totals but also to check movements by time of day, which makes it easier to narrow down causes. For example, if generation is high relative to irradiance in the morning but drops sharply only in the afternoon, possible causes include afternoon shading, the impact of rising temperature, output limits applied at specific times, or equipment reducing output due to temperature increase. Conversely, if generation is lower by almost the same proportion throughout the day, check for panel soiling, differences in measurement conditions, overall degradation of equipment efficiency, and the pyranometer’s installation condition.

When looking at power generation relative to solar irradiation, it is useful to check not only the plant’s total output but also the output per unit of installed capacity and the output for each section within the same plant. If you compare systems with different capacities directly, larger systems will appear to generate more power. Viewing output per unit of capacity makes it easier to compare differences in generation performance. When there are sections on the same site with similar orientation and tilt, their solar irradiation conditions can be considered similar, so if only a particular section shows lower output, it becomes easier to narrow the investigation down to that section.

One thing to note is that the solar irradiance data itself can be subject to measurement errors and influenced by installation conditions. If the pyranometer is dirty, not tilted to match the panel surface, shaded, or installed at a location that is not appropriate as a representative point, the irradiance value may not adequately reflect the actual conditions at the panel surface. Even when power output appears low relative to the measured irradiance, it is important to inspect not only the generation equipment but also the condition of the irradiance-measuring instrumentation.

Also, discrepancies between solar irradiance and power generation are easier to assess if you separate short-term changes from long-term changes. If there is a large deviation on only one day, temporary clouds, snowfall, fallen leaves, planned work outages, communication loss, or output control may be involved. On the other hand, if generation consistently fails to increase on sunny days, you should suspect dirt accumulation, changes in shading, degradation of panels or equipment, or circuit abnormalities. Distinguishing between isolated anomalies and continuous declines also makes it easier to set inspection priorities.

To embed in operations how to interpret power output relative to solar irradiance, it is useful to understand what level of power output is typical on days with sufficient irradiance. If you have a benchmark for clear-sky conditions, it becomes easier to determine whether a perceived low power output is due to weather-related variation or to changes on the plant side. When multiple people are responsible for viewing monitoring data, establishing a rule to review irradiance and power output together can also reduce inconsistencies in judgment.

Approach 2: Identify Declining Trends by Comparing with Past Data

To detect a decline in power generation early, it is essential to compare the current power output with past data. Because solar power generation varies by season, simply comparing it with the previous day or the previous month may not give an accurate assessment. When making comparisons, it is important to choose data with similar conditions—such as the same month of the previous year, the same season, days with similar weather, or trends since the start of generation. In particular, when you feel that "power output is low," you need to distinguish whether it is a short-term dip or a long-term decline.

Comparing the same month with the previous year is a way of viewing data that makes it easier to account for seasonality. For example, rather than comparing power generation in May with January, comparing May with May of the previous year provides sunshine duration and solar altitude conditions that are closer. However, even year‑over‑year month‑to‑month comparisons can show differences in power generation if the weather conditions differ greatly. If the previous year was consistently sunny and this year has been consistently cloudy, it is natural for the generation to appear lower. Therefore, when reviewing historical data you should also check solar radiation and weather trends.

In historical comparisons, what you should pay attention to is not only the absolute value of power generation but also how the decline progresses. If generation suddenly drops around a specific day, suspected causes include equipment shutdown, poor connections, breaker trips, communication failures, or the impact of construction or power outages. On the other hand, if it gradually decreases over several weeks to months, likely causes include soiling of the panel surface, shading from plant growth, changes in the surrounding environment, dirt accumulation due to poor drainage, and progressive degradation of equipment. The locations to inspect and the priority of responses differ between sudden declines and gradual declines.

When reviewing historical data, it's effective to check not only monthly generation but also daily and hourly data. Relying solely on monthly generation can mask outages lasting several days or drops during specific hours because they get averaged out. By using daily data to find the day when the decline began and hourly data to identify the time periods when output dropped, you can narrow down potential causes. For example, if output is low every afternoon starting on a certain day, seasonal changes in the position of shadows may be the cause. If there is a large drop only on a specific day, it's worth checking that day's work logs and alert history.

When comparing with past data, also verify that equipment conditions have not changed. Additions of panels, equipment replacements, setting changes, changes to measurement points, updates to monitoring devices, or changes in power contracts or grid interconnection conditions can make simple comparisons with past data difficult. A reduction in apparent generation may actually be due to changes in measurement methods or aggregation scope. Confirming in advance that the data collection methods and equipment configuration are the same will help prevent misjudgments.

Comparisons with past data not only help detect anomalies but also assist in explaining inspections. When requesting on-site response, if you can organize the situation as "lower compared with the same month last year," "lower compared with days that had similar levels of solar irradiance," or "declining since a specific date," it becomes easier to communicate with stakeholders. Presenting it as a change in monitoring data rather than saying it feels low makes it easier to share the need for inspection and to prioritize responses.

Perspective 3: Look at the differences between power conditioner units

If you look only at the total power generation of a power plant, you can miss localized faults. Even if the overall drop doesn't appear significant, a particular power conditioner may not be generating at all or may be producing noticeably less than the others. To detect such conditions early, it is important to compare generation and output per power conditioner as well as the plant-wide totals.

When comparing power conditioner units, first compare devices with the same capacity, the same orientation, and the same installation conditions. If the conditions are similar, their output trends on sunny days are also expected to be similar. If a particular unit has lower generation, you need to check the panel array connected to that unit, the circuits, protective devices, settings, shutdown history, temperature conditions, and so on. If only one unit among multiple units is not generating, it may be easy to overlook in the total generation, but looking at each device separately makes it easier to identify as a potential anomaly.

However, differences in power output between power conditioners are not necessarily all abnormal. Natural variations can arise from equipment conditions such as different capacities of the connected panels, different orientations and tilts, different patterns of shading, different temperature conditions at the installation site, and differing extents of influence from grid constraints. Therefore, when making comparisons, it is important to equalize the connected capacity and installation conditions rather than judging solely by the absolute size of the power generation.

When viewing at the power-conditioner unit level, it is useful to check both daily generation and hourly output. If you find a unit with low daily generation, check that unit’s hourly output. Depending on whether it produced no output from the morning, stopped partway through, only dips at midday, or its output plateaus during sunny conditions, the suspected causes change. If it is stopped for the entire day, suspect a shutdown condition or protective device. If it drops partway through, check temperature, the grid/system, abnormal shutdowns, settings, and connection status. If it is low only at certain times, consider shading and the surrounding environment.

It is also important to review this in combination with the alert history. If a power conditioner has stopped, monitoring data may show records of anomalies or stoppages. However, not every anomaly will necessarily appear as a clear alert. Even if no alert has been issued, if power generation remains lower than that of other equipment, it should be treated as an anomalous sign in the monitoring data. Conversely, because an alert may be present while its effect on generation is small, judgments should be made by looking at both the history and the actual generation.

If you make it standard practice to monitor differences at the power conditioner unit level, you can pick up anomalies before the entire plant’s output declines significantly. In particular, at large-scale facilities the shutdown or low output of a single unit can be masked by the overall figures. During daily checks, even simply confirming each device’s generation ranking and its deviation from the average helps with early detection. When you receive a report of low generation, first look at the overall picture and then break it down to the power conditioner unit level; establishing this workflow makes it easier to isolate the cause.

Perspective 4: Detect localized performance drops using string- and circuit-level data

When a potential anomaly is found at the power conditioner level, you then check data at the string and circuit levels in more detail. If you can view string-level current, voltage, power generation, and status values, it's easier to detect performance drops affecting part of a panel array. Differences that appear small at the plant-wide or equipment level can show up as clear anomalies at the string level.

What you should pay attention to in string- or circuit-level data is the variation among circuits under the same conditions. For example, when multiple circuits consist of panels with the same orientation, the same tilt, and the same number of modules, they are expected to show similar generation trends. If a particular circuit shows lower current, lower energy production, or extreme drops at certain times of day, candidates to check include soiling, shading, wiring problems, poor connections, partial panel faults, and the condition of protective devices.

A drop in current is especially important to watch when output does not increase despite receiving solar irradiance. Generally, under similar irradiance conditions and with the same circuit configuration, current values tend to be similar. If only one string shows a lower current, possible causes include that string being shaded, its surface being dirty, influence from weeds or nearby structures casting shadows, or problems at the connections. However, because appearances vary depending on circuit configuration and measurement methods, it is necessary to make judgments while cross-checking with the design information.

Interpreting voltage is also important. If the voltage is extremely low or behaving differently than usual, you need to check for circuit isolation, connection problems, differences in the number of panels, and the control state on the equipment side. However, because voltage varies with temperature and the operating point of the equipment, you should avoid concluding the cause based on a single value alone. Looking at current, voltage, generated power, alerts, and time-of-day curves together increases the accuracy of your assessment.

String- and circuit-level data also help detect shading and soiling. Surface soiling and partial shading of panels are often identified as causes of low power output. Bird droppings, dust, fallen leaves, pollen, yellow dust, drainage marks, weeds, nearby trees, and shadows from mounting racks or fences can affect only specific circuits. If monitoring data show a persistent decline in a particular circuit, on-site checks of the panel positions corresponding to that circuit make it easier to narrow down the inspection area.

On the other hand, because string-level data are more detailed, care must be taken to avoid misreading the data. If the names of monitored items, circuit numbers, on-site wiring numbers, and positions on the drawings do not match, there is a risk of incorrectly identifying the location of the degradation. Before inspection, it is important to confirm the correspondence between the numbers on the monitoring screen and the on-site equipment. Even if a drop in power generation is detected early, if the corresponding location cannot be identified on site, responding will take time. Managing the linkage between monitoring data and on-site information is critically important in actual operations.

View 5: Identify abnormal time periods from time-of-day power generation curves

The generation curve by time of day is useful for pinpointing the causes of a drop in power output. Daily generation alone doesn’t tell you when the decline occurred. To determine whether output was low from the morning, failed to rise at midday, dropped only in the afternoon, or stopped partway through, you need to look at the output trend for each time period. By examining the shape of the generation curve, you can interpret the pattern of the decline rather than merely noting low generation.

The power generation curve on clear days typically rises in the morning, peaks around midday, and then declines toward the evening. On days with many clouds it fluctuates frequently, but if the curve shape is extremely distorted on a clear day, it is a trigger to suspect effects from the equipment or the surrounding environment. For example, if the rise is delayed only in the morning, possible causes include shadows on the east side, fog, remaining snow, startup conditions, or missing communication data. If it is low only in the evening, candidates include shadows on the west side, surrounding trees, topography, or the influence of nearby structures.

When output suddenly drops around midday, multiple causes need to be checked. These include output curtailment due to equipment temperature rise, the influence of grid voltage, output control, protective operation, communication failure, and circuit abnormalities. If output plateaus during sunny periods with strong solar irradiance, the relationship between installation capacity and equipment capacity, output limit settings, and the operating state of equipment should also be checked. However, because output may be limited within ranges assumed in the design, it is important to verify the design conditions and operational conditions before judging the situation to be abnormal.

When looking at generation curves, it becomes easier to spot anomalies if you overlay not only the overall plant curve but also curves by power conditioner unit and by circuit. Even if the overall curve appears to show natural variation, a specific piece of equipment may have dropped out midway. Compare the shape of the curves among equipment under similar conditions; if only one unit behaves differently, prioritize checking that unit. If multiple units drop out at the same time, consider influences such as the grid side, output control, communications, shared equipment, and weather conditions.

Power generation curves are also useful for checking for shadows. The effects of shading manifest differently depending on the time of day. If you observe patterns such as generation dropping at the same time every day, the period of reduced output shifting as the seasons progress, or only specific circuits dropping at the same time, it is worth checking for shadows from nearby trees, buildings, utility poles, fences, slopes, or adjacent equipment. Shadows can sometimes be identified by visiting the site, but if monitoring data can narrow down which time periods should be checked on site, inspection efficiency will improve.

When looking at time-based data, attention must also be paid to the data's granularity. If recorded at short intervals, sudden stoppages or resumptions are easier to detect, but when aggregation intervals are long, temporary anomalies are averaged out and become harder to see. Rather than judging based only on daily or monthly reports, checking hourly and, if necessary, finer-grained data makes it easier to pinpoint when a drop in power generation occurred. Understanding the granularity of monitoring data is also essential for early detection.

View 6: Compare alert history with on-site conditions

When checking for a drop in power generation using monitoring data, the alert history provides an important clue. Histories detected by monitoring devices—such as equipment shutdowns, communication failures, voltage anomalies, temperature anomalies, insulation-related abnormalities, and grid-related abnormalities—can help narrow down the cause. If the days or periods with low power generation coincide with alert timestamps, it is worth verifying a possible connection to the generation drop.

However, an alert does not necessarily indicate the primary cause of reduced power generation. For example, in the case of communication failures, the system may actually be generating power but data cannot be retrieved. Conversely, even if no alert is issued, factors such as panel soiling, shading, weeds, minor connection faults, and gradual degradation trends may first appear as reduced power output. Therefore, it is important not to rely solely on alert history but to assess power generation, solar irradiance, equipment-level comparisons, circuit-level data, and generation curves together.

When reviewing alert history, check the occurrence time, duration, recovery time, affected equipment, and recurrence frequency. The priority of response differs between a history that occurred once for a short time and one that repeats at the same time every day. If the system returns to normal quickly and the impact on power generation is small, it may be acceptable to monitor the situation; however, if the same equipment is repeatedly affected or it occurs simultaneously with a drop in power generation, an on-site inspection should be considered. It is important to assess not only the number of occurrences in the history but also the impact on power generation.

Cross-checking with on-site conditions is also indispensable. When a decline is detected in monitoring data, on-site checks are made for panel surface soiling, sources of shading, weed growth, snow or fallen leaves, drainage around the mounting structure, the condition of cables and junction boxes, equipment displays, breaker status, ventilation and temperature conditions, and so on. Even if a decline looks similar in the monitoring data, the on-site causes can differ. For example, if output is low only in the afternoon, it may be caused by shading, but it can also be related to equipment temperature or effects from the grid side.

Before proceeding to on-site inspection, it's efficient to narrow the inspection scope as much as possible using monitoring data. If you determine whether the whole plant is underperforming, a specific power conditioner, a particular string, or a specific time period, it becomes easier to decide the order of checks on-site. Conversely, if you go to the site without prior preparation, it will take time to locate the problem area within a large plant. Monitoring data can be used not only to reduce on-site inspections but also to improve their quality.

Also, be sure to record alert histories and the results of on-site responses so they can be used for future decisions. By accumulating data on which alerts actually led to decreases in power generation, which decline patterns corresponded to soiling or shading, and which equipment repeatedly exhibited abnormalities, it becomes easier to judge when similar monitoring data appears next time. Early detection of generation declines improves in accuracy through operational repetition of monitoring, inspection, recording, and improvement, rather than through one-off verification tasks.

Operational approach for linking monitoring data to inspection decisions

Monitoring data, if merely glanced at, will not lead to early detection of declines in power generation. What matters is deciding in advance which data to check, how often, and by what criteria, and what actions to take when a potential anomaly is found. Rather than starting to frantically review data after you notice low power generation, you need to establish operating practices that can pick up signs of decline during routine monitoring.

First, decide which items to check daily. Items that are easy to include in daily checks are the plant’s total daily power generation, its relationship with solar irradiance, generation per power conditioner unit, alert history, and communication status. For daily checks, it is not necessary to pinpoint detailed causes. What matters is quickly detecting any behavior that deviates from normal. If there are no suspected anomalies, record it as a normal entry; if there are suspected anomalies, proceed to hourly data and circuit-level data.

Next, decide which items to review on a weekly and monthly basis. On a weekly basis, check trends in power generation, variations by equipment, recurring alerts, and declines during specific time periods. On a monthly basis, examine year-on-year comparisons for the same month, monthly power generation trends, changes in power generation relative to solar irradiance, and relationships with cleaning, weeding, and inspection histories. Gradual declines that are hard to detect in short-term monitoring are easier to discover when viewed weekly or monthly.

Sharing the criteria for judgment is also important. If different personnel have different standards for what feels “slightly low,” responses can be delayed or unnecessary checks can increase. For example, it is helpful to organize internally the conditions that make a system a candidate for inspection—such as when there is an obvious difference compared with equipment under the same conditions, when power generation does not increase relative to solar irradiance on sunny days, when declines recur at the same time of day, or when stop/shutdown history continues. When setting numerical criteria, it is also important to consider equipment conditions and seasonal variations and to set levels that are not excessively strict but are unlikely to be overlooked.

Linking monitoring data with on-site photos, drawings, and inspection records also helps with the early detection of declines in power generation. Even if the monitoring screen shows which equipment or circuit is showing a decline, if you don’t know where it is on site, responding will take time. By organizing equipment numbers, circuit numbers, panel layouts, inspection photos, and past response histories, you can quickly locate the affected area. Mapping data to on-site locations is especially important when managing large power plants or multiple sites.

In responding to reduced power generation, it is also necessary to avoid definitively attributing the cause based on monitoring data alone. Monitoring data is a powerful resource for narrowing down causes, but ultimately judgment should be made by combining it with on-site conditions, equipment configuration, weather conditions, and operational history. What looks like soiling in the data may actually be a shadow; what appears to be equipment failure may be a communication loss; what seems like an overall drop may be output control. In practice, it is safer not to rush to conclusions and to organize and verify candidate causes.

To improve monitoring operations, post-incident reviews after detecting a drop in power generation are indispensable. If detection was delayed, check which data would have allowed earlier awareness. If there are many unnecessary dispatches, review whether the decision criteria are ambiguous. If it took a long time to identify the cause, check the correspondence table between monitoring data and on-site information, and the organization of photos, drawings, and inspection records. By repeatedly implementing such improvements, monitoring data will transform from mere records into decision-making material for power plant management.

Summary

To detect declines in power generation early, it is important to view monitoring data from multiple perspectives. Rather than judging low output solely by daily generation, combining generation relative to irradiance, comparisons with past data, differences between power conditioner units, variations by string or circuit, time-of-day generation curves, and alert history with on-site conditions makes it easier to narrow down candidate causes for the decline.

In practice, it is important to distinguish whether a decline in power generation is occurring across the entire system, only in certain pieces of equipment, only during specific time periods, or whether it is persistent. Abnormalities that cannot be seen from total generation alone become easier to detect when the data is broken down by device, by circuit, and by time period. Conversely, if you draw conclusions from only a portion of the monitoring data, you risk misidentifying weather variations, communication losses, output control, or differences in installation conditions as anomalies.

When you feel that power generation is low, it is effective to first check the relationship with solar irradiance, compare it with past data under the same conditions, and then break it down by equipment level, circuit level, and time of day. By cross-referencing alert history, work history, on-site photos, drawings, and inspection records, it becomes easier to narrow down the areas that need inspection. Creating a system that links monitoring data to on-site response reduces the chance of overlooking drops in power generation and makes it easier to prioritize actions.

Moreover, the accuracy of early detection improves through daily operations. Decide which items to monitor on a daily, weekly, and monthly basis, share the decision criteria, and record anomaly candidates and the results of on-site inspections; doing so will speed up decision-making in subsequent cases. When power generation declines, it is important not only to respond after it occurs but also to detect early signs and verify them before the impact becomes significant.

When organizing how monitoring data is presented, it's reassuring to have in place a system that can handle power generation, solar irradiance, equipment-specific data, alert history, site photos, drawings, and inspection records without treating them separately. If you want to detect declines in power generation early and streamline on-site responses, it's important to begin by rethinking operational practices to link monitoring data with on-site management.