6 Checks to Diagnose the Causes of Low Power Generation Using Data

When you feel that power generation is low, the first thing you need is not to assume the cause based only on your impression. The power output of a solar power plant varies due to multiple factors such as weather, solar irradiance, temperature, equipment condition, missing monitoring data, grid-side constraints, shading, soiling, and installation conditions. Judging only by appearances like "lower than yesterday," "lower than last month," or "lower than expected" cannot determine whether it is an abnormality or a natural fluctuation.

In practice, it is important not only to look at power generation itself but, after standardizing the basis for comparison, to break down and check from which point, over what range, and to what extent it has declined. Being able to judge declines in power generation from the data makes it easier to prioritize on-site inspections and to narrow down the cause while reducing unnecessary investigations.

Table of Contents

• The first things to consider when you feel the power generation is low

• Method 1 Look at the relationship between power generation and solar irradiance

• Method 2 Compare with past data to identify when the decline began

• Method 3 Look at differences between strings and sections to narrow the abnormal area

• Method 4 Consider temperature and seasonal conditions when evaluating generation efficiency

• Method 5 Check the history of downtime and output curtailment

• Method 6 Cross-check on-site conditions with data to isolate causes

• Mistakes to avoid when checking data

• Summary A decline in power generation can be narrowed down by comparing records

First things to check when you feel power generation is low

The amount of electricity generated by solar power is not constant from day to day. Even for plants with the same installed capacity, generation varies depending on sunny days, cloudy days, rainy days, the flow of clouds in the morning and evening, seasonal solar altitude, temperature, and the presence or absence of wind. Therefore, simply saying “generation is low today” or “this month is lower than last month” alone does not necessarily indicate an equipment fault.

To determine the cause of low power generation, you must first align the comparison conditions. For example, comparing generation on a rainy day with that on a sunny day is insufficient as a basis for judging an anomaly. Even on sunny days, solar irradiance and temperature differ between spring and summer, and between summer and winter. Furthermore, a day that was cloudy only in the morning and a day that was steadily sunny throughout the day will produce differences in the daily total generation.

The first thing operations personnel should do is classify the low power generation by "when", "where", "how much", and "compared to what". Is the output low across the entire plant, or only in certain sections? Is it low for a single day, or has it persisted for several weeks? Is it low only during specific hours of the daytime, or for the whole day? By separating these factors, it becomes easier to determine whether the variation is a natural fluctuation due to weather or a problem with equipment or management.

Also, reductions in power generation can be divided into those you cannot determine without going to the site immediately and those you can narrow down to some extent using only monitoring data. For example, if communications have stopped or there are measurement gaps, the recorded generation may appear low rather than the actual generation. If only certain PCS units are low, you can limit your checks to that range. If output drops only in the morning, shading or the condition of equipment on the east side may be involved.

The important point is not to pin the cause on a single factor, but to check in the order of the data. By looking, in sequence, at solar irradiance, historical comparisons, differences between sections, temperature conditions, shutdown history, and on-site conditions, you can more objectively organize the reasons for low power generation. Below, we explain six checks that practitioners can easily use for daily inspections and monthly reviews.

Method 1: Look at not only power generation but also its relationship with solar irradiance

When you feel that power generation is low, the first thing to check is its relationship with solar irradiance. Because solar power generation converts sunlight into electricity, it is common that lower irradiance leads to lower power generation. In other words, rather than judging the output as low based solely on the generated power, it is important to also look at how much solar energy input there was that day.

For example, even if power generation has dropped significantly compared with the previous day, if solar irradiance also decreased on the same day, it is more likely due to weather rather than an equipment fault. On the other hand, if solar irradiance is roughly the same but only power generation has declined, you need to consider possible causes on the equipment side, the measurement side, or the operation side. The first step in determining a decline in power generation from data is to check whether changes in power generation and solar irradiance are moving in the same direction.

When examining the relationship with solar irradiance, it is easier to make a judgment if you check not only the daily total but also changes by time of day. If power generation is increasing normally relative to irradiance in the morning but is low only in the afternoon, possible causes include afternoon shading, rising equipment temperature, shutdown of specific equipment, or output restrictions. Conversely, if generation is low only in the morning, check for shading from the east, frost, morning dew, delayed startup, or a shift in the measurement start time.

When using irradiance to make judgments, you also need to check the condition of the pyranometer installed at the plant. If the pyranometer is dirty, its tilt does not match the actual panel surface, or communications are interrupted, the irradiance data itself may be incorrect. If irradiance is shown as abnormally low, it may be the irradiance data that is faulty rather than the power generation.

Also, by continuously monitoring the relationship between solar irradiance and power generation, it becomes easier to notice soiling and changes in the surrounding environment. If, when comparing days with similar irradiance conditions, the power generation is gradually declining rather than affected by short-term cloud cover, panel surface soiling, weed growth, increased shading, or changes in equipment efficiency may be behind it. In particular, comparing days in the same season, under similar clear-sky conditions, and at the same time of day makes it easier to detect such changes.

When determining the cause of low power output, comparing it to solar irradiance is a basic check. Rather than concluding an anomaly based solely on generation output, assessing whether the output is appropriate relative to solar irradiance makes it easier to distinguish weather-related factors from equipment-related factors.

Method 2: Identify the start of a decline by comparing with past data

To investigate the cause of a drop in power output, it is important to check when the decline began. Whether it is suddenly low on a single day, has been low for several days, or is gradually decreasing over several months will change the possible causes. Comparing with past data helps determine whether the drop in power output should be seen as a temporary fluctuation or treated as a persistent anomaly.

If power output suddenly drops, it may be related to specific events such as equipment shutdown, communication failures, operation of circuit breakers, grid-side constraints, stoppages due to construction or inspection, or malfunctions of measurement instruments. If you can identify the date and time when the decrease occurred, you can check work records before and after, alarm histories, whether any on-site work took place, and changes in the weather. If the power output graph shows a step-like drop, it is easier to assume that the condition changed at a certain point in time.

On the other hand, when power generation is gradually declining, causes that develop over time can be considered, such as accumulation of dirt, shading from vegetation growth, aging of equipment, deterioration of connections, and changes in the condition of panel surfaces. In such cases, because it is difficult to notice the change by comparing only with the previous day, comparisons on a weekly or monthly basis and with the same month of the previous year are effective. In particular, comparing with past data for the same month helps reduce seasonal variation to some extent when making judgments.

When comparing with past data, we look not only at total power output but also at the generation curve for each time of day. On sunny days with stable solar irradiance, output tends to ramp up in the morning, peak around midday, and decline in the evening. If the curve flattens partway, dips only at specific times, has a delayed morning ramp-up, or an early evening decline, it becomes easier to narrow down the likely causes.

When reviewing historical data, be careful in choosing what to compare. Simply comparing the same date from the previous year can lead to incorrect conclusions if the weather that day was different. If possible, use as comparisons clear-sky days from a similar season, days with similar solar radiation, or days when the equipment configuration was the same. If equipment was expanded or settings were changed partway through, comparing the before-and-after as-is can produce large errors.

It is also effective to cross-reference the start of the decline with management records. If there were weeding, cleaning, inspections, equipment replacements, grid-related work, changes to communication equipment, or changes to monitoring settings, check whether the power generation data changed before and after those activities. If the changes in the data match the work history, it becomes easier to explain the cause. Conversely, if a sudden decline appears without any work history, you should suspect overlooked equipment abnormalities or external factors.

To determine the cause of low power generation from data, it is important not only to recognize that output has fallen but also to pinpoint when the decline began. Looking at the data as a time series lets you distinguish whether the issue is a sudden anomaly or a gradually developing problem.

Method 3 Narrow down the abnormal range by looking at differences between strings or sections

Even if the power output of the entire plant appears low, it does not necessarily mean the whole plant has uniformly declined. It may be only certain strings, some PCS units, or particular sections that are underperforming. In such cases, if you only look at the plant-wide total, you are likely to overlook the abnormal areas. To efficiently identify the cause, it is important to compare generation data at the finest possible granularity.

If current and voltage values for each string can be checked, compare strings that are under the same conditions. With the same orientation, the same tilt, the same number of panels, and the same equipment conditions, generation trends will generally show similar behavior. If one particular string shows lower current, abnormal voltage, or no output, there may be a fault, shading, or connection problem in that area.

The same applies when examining on a per-section basis. If the power plant has multiple areas, check whether all sections are similarly low or only specific sections are low. If all sections are low at the same time, you are more likely to suspect weather, solar irradiance, grid-side restrictions, overall settings, or monitoring data issues. On the other hand, if only some sections are low, you can focus inspections on issues limited to those sections such as shading, soiling, weeds, equipment shutdowns, wiring, connections, panel condition, etc.

Differences between PCS units are also an important factor in decision-making. If power generation and output can be seen for each PCS, compare PCS of the same capacity on a daily and hourly basis. If a particular PCS is generating less power, give higher priority to checking that PCS’s input side, output side, settings, shutdown history, cooling condition, and surrounding environment. If multiple PCS units decline at the same time, consider common systems or upstream factors.

However, when making comparisons, it is necessary to take differences in equipment conditions into account. In sites where panel orientation or tilt differs between sections, PCS capacities differ, locations are prone to shading, or the power plant has undulating terrain, you cannot judge based on a simple comparison of generation alone. In such cases, you should normalize generation by installed capacity or compare only sections with the same conditions to separate apparent differences from actual anomalies.

Narrowing down the abnormal range improves the efficiency of on-site inspections. Rather than walking the entire power plant to search for the cause, if you first identify on the data the sections, time periods, and equipment that show declines and then carry out on-site inspections, you can more easily shorten inspection time. Especially at large-scale power plants, narrowing the scope with data before on-site inspections affects both the accuracy of root-cause analysis and operational efficiency.

When power generation is low, it's important to pay attention not only to the overall values but also to variations among individual units. If you can pinpoint where the output is low, it becomes clear what needs to be checked.

Method 4: Assess Power Generation Efficiency by Considering Ambient Temperature and Seasonal Conditions

Solar power generation does not necessarily reach its maximum just because irradiance is high. Air temperature and panel temperature also affect power output. In general, photovoltaic modules tend to lose output as temperature rises. Therefore, even on a sunny summer day with high irradiance, generation may not increase as much as expected. This is not necessarily an abnormality; it can be a natural variation due to temperature conditions.

When determining the causes of low power generation, check not only solar irradiance but also ambient temperature. On clear days in spring and autumn, the balance between irradiance and temperature conditions is often favorable, so generation tends to be higher. Conversely, in midsummer, even with high irradiance the panel temperature tends to rise, reducing generation efficiency. In winter, low temperatures can be advantageous for efficiency, but because daylight hours are short and the sun's elevation is low, the daily total generation is influenced by seasonal conditions.

If you judge a situation abnormal simply because summer generation doesn’t increase as much as expected or winter generation is low, without understanding these seasonal differences, it can lead to incorrect responses. What is necessary is to compare with days under similar conditions, taking into account temperature and seasonal factors. For example, comparing sunny days in midsummer with one another, comparing with past data from the same month, or comparing days with similar temperatures are effective methods.

When assessing power generation efficiency, check the relationship between generated energy and installed capacity, and between generated energy and solar irradiance. If power plants differ in scale, simple generated energy cannot be directly compared. Converting to energy generated per unit of installed capacity makes it easier to compare power plants or sections. Also, by looking at how much energy is generated relative to solar irradiance, you can partially account for differences in weather.

The effect of air temperature also shows up in time-of-day graphs. In the morning, when air temperature is lower, generation efficiency is comparatively good; however, after midday, as panel temperature rises, output may not increase much even when solar irradiance is high. A slightly suppressed midday generation curve should not be immediately judged as an anomaly. However, if output is clearly lower compared with days that have similar temperature conditions, you should consider other causes.

In addition, there are seasonal factors that can affect power generation, such as snow accumulation, frost, yellow sand (Asian dust), pollen, fallen leaves, bird damage, and dust from nearby construction. These can sometimes be difficult to determine from solar irradiance data alone. For example, if it is sunny and solar irradiance is present but power generation is low, on-site factors may be hidden, such as part of the panel surface being dirty, snow remaining, or shadows from vegetation extending.

By taking into account temperature and seasonal conditions, it becomes easier to distinguish between natural variations in power generation and abnormal declines. Before judging that generation is low, it is important to confirm whether the output is reasonable for that time of year and whether it is lower even when compared with similar conditions.

Method 5 Check the history of downtime and output suppression

The causes of low power generation are not limited to a decline in the performance of the generation equipment itself. Histories such as temporary equipment shutdowns, output restrictions, or interruptions in communications or measurements can affect generation. When checking days with low generation, it is important to review not only the generation curve but also downtime, alarms, and control history.

The first thing to check is the shutdown history of the PCS and related equipment. Even on days with low power generation, the impact differs depending on whether the stoppage lasted minutes or hours. If the stoppage was brief, its effect on the daily total may be limited, but if it occurred during periods of high solar irradiance, the reduction in generation will be greater. If stoppages are concentrated around midday, they can significantly affect the daily total generation and may be the primary cause of the generation drop.

Next, check the history of output curtailment and control. Even when the facility is capable of generating power normally, output can be curtailed by external conditions or control commands. In such cases, it does not mean that the power plant’s equipment is faulty; the generation may appear low as an operational constraint. If the generation curve is capped at a constant upper limit, or if there are periods when output does not increase even though solar irradiance rises, you need to verify whether any control is in effect.

Communication errors and measurement data gaps are also causes that are easy to overlook. Even if generation is actually occurring, if data is not being transmitted to the monitoring device, the recorded generation may appear low. If the data suddenly reads zero, is missing only during specific times, or some records are missing while other devices are normal, suspect communication or measurement problems rather than the generation equipment. Before treating a decrease in generation as an on-site anomaly, it is important to check for missing data.

When checking the shutdown history, align the time of the drop in power output with the timestamps in the history. If the time when power generation fell matches the time the equipment stopped, it is a strong candidate for the cause. On the other hand, if there is no shutdown history but only a drop in power output, you need to look for other factors such as shading, dirt, temperature, poor connections, or abnormal measurements. By aligning the times, you can more easily distinguish between a coincidental match and the actual cause.

Also check the history of intentional shutdowns for inspections and construction. If on-site work records are managed separately from monitoring data, a later drop in power generation can appear to have an unknown cause. In monthly reports and power generation assessments, it is important to link records of inspection shutdowns, construction shutdowns, grid-side work, and communication work to the generation data.

To determine the cause of low power generation, it is necessary to distinguish whether there were periods when no power was generated, whether generation was curtailed despite the ability to generate, or whether it was simply not recorded. Checking downtime and control history is an important step to avoid misidentifying the cause.

Method 6 Cross-check on-site conditions with data to isolate the cause

Once data analysis reveals the likely direction of the cause, the next step is to cross-check with on-site conditions. Monitoring data is useful, but there are factors—shadows, dirt, vegetation, bird damage, physical damage, drainage conditions, nearby construction, snow accumulation, fallen leaves, and so on—that are difficult to judge from numbers on a screen alone. To more reliably narrow down the causes of low power output, it is important to link anomalies seen in the data with conditions visible on site.

For example, if data shows reduced power generation only in the afternoon, on-site we check for shadows cast by trees or buildings extending to the west, utility poles, and around the mounting racks. If only a specific section has low generation, we focus on that section’s panel surfaces, grass height, drainage conditions, cable condition, areas around junction boxes, and the tilt of the mounting structures. By narrowing the anomaly range with data before conducting on-site inspections, we can reduce unnecessary investigation.

During on-site inspections, it is important to record photos and location information. If the section judged to have low power generation corresponds to the shadows or dirt confirmed on site, it becomes easier to explain the cause. Conversely, if there are on-site photos but it is unclear which power generation data they correspond to, they are difficult to use later as material for assessment. On-site records should be organized on the same timeline and at the same sectional level as the power generation data.

Also, even if you find something that appears abnormal on site, you need to confirm with data whether it is the primary cause of the drop in power generation. For example, even if part of a panel is dirty, if that does not coincide with the time periods or areas of reduced generation, it may not be the main cause. Even if grass has grown, if the period during which it actually casts a shadow is short, the impact on power generation may be limited. It is important not to assume that phenomena observed on site are the cause, but to verify consistency with the data.

When comparing on-site conditions with data, continuity of records is also important. A single field inspection cannot fully capture seasonal changes in shadows, vegetation growth, or the accumulation of dirt. Regularly recording the same position, same direction, and same subject makes it easier to track the factors behind reduced power generation over time. In particular, the shadows cast by weeds and trees can change over weeks to months, so fixed-point recording is effective.

Also check the generation data after on-site work. By seeing whether generation has recovered after cleaning, weeding, equipment restoration, and connection checks, you can determine whether the actions taken were effective. Comparing days with similar solar irradiance conditions before and after the work makes it easier to demonstrate the improvement. Confirming not only that a response was carried out but also how generation changed afterward helps prevent recurrence and improves the quality of reporting.

Data is a tool for narrowing down causes, and on-site inspection is the process of verifying those hypotheses. By combining both, you can make a more objective judgment about the causes of low power generation.

Mistakes in judgment to avoid when checking data

When checking for a drop in power generation, even when you think you are looking at the data, the assumptions behind your judgment can be misaligned. A typical example is determining an anomaly based only on the total generated power. If you compare only daily totals without looking at insolation, temperature, downtime, system capacity, or section conditions, you may misidentify weather differences or seasonal variations as equipment faults.

Also, caution is needed when making judgments based on only short-term data. Solar power generation is affected by weather fluctuations, so a decline observed on a single day can be difficult to determine as abnormal. Of course, an obvious shutdown or a sudden drop requires prompt inspection, but when assessing slight differences, examining data over multiple days, multiple weeks, and the same period in past years improves accuracy.

Care must also be taken in how comparison targets are chosen. If you compare areas with different system capacities, different shading conditions, or different panel orientations using the same criteria, you can mistake normal differences for anomalies. Before comparing, confirm whether they can be compared under the same conditions. If the conditions necessarily differ, it is important to normalize the data into forms that are easy to compare, such as per unit capacity, per unit solar irradiance, or by time of day.

Missing data can sometimes be mistaken for a drop in power generation. If there are periods when monitoring data are interrupted, communications are unstable, or measuring instruments are offline, the recorded generation can appear lower than the actual output. In such cases, even if the on-site equipment’s generating capability is fine, reports and monitoring screens may show low generation. If generation suddenly falls to zero or records look unnaturally flat, first verify the reliability of the data.

Also, it is a judgment to avoid narrowing the cause down to just one. A decline in power generation can result from multiple overlapping factors. When light soiling, partial shading, high temperatures, and brief stoppages coincide, each may have only a small effect individually, but together they can cause a non-negligible reduction. When reviewing the data, it is important to identify the primary cause while also organizing any contributing factors.

Finally, failing to record the inspection results is also a major risk. Even if you investigate the cause of reduced power generation, if the rationale for decisions at that time, the verified data, on-site photos, the work performed, and the results after improvement are not retained, they cannot be used when the same problem occurs again. Confirmation of a decline in power generation should not be a one-time response but should be treated as an ongoing record to improve the quality of plant management.

Summary: The cause of decreased power generation can be narrowed down by comparing records

When you notice low power output, rather than immediately concluding an equipment fault, it is important to check the data step by step. First, examine the relationship between power output and solar irradiance to confirm whether it is a natural fluctuation due to weather. Next, compare with historical data to identify when the decline began. Then, by looking at differences across strings, PCS units, and sections, you can determine whether the anomaly is affecting the entire plant or is limited to a specific part.

Furthermore, by taking ambient temperature and seasonal conditions into account, it becomes easier to distinguish natural efficiency degradation from abnormal drops in power generation. Checking downtime and output curtailment histories can reveal that the issue is due to operational constraints or record-keeping problems rather than equipment faults. Finally, by matching the data-filtered areas with on-site conditions, you can narrow down the specific causes such as shading, soiling, weeds, and equipment condition.

When judging a decrease in power generation, it is important not to rely on a single number. Combining power output, solar irradiance, time of day, differences between sections, temperature, shutdown history, and on-site records can greatly change the diagnosis of the cause. In particular, in practice it is important to be able to explain which data you looked at and why you suspected that cause. If you can perform explainable management, internal reporting, coordination with maintenance companies, explanations to owners, and consideration of recurrence prevention will be easier to carry out.

The cause of low power generation can be only the weather, or it can originate from equipment or the local site environment. The important thing is not to look only at the low result, but to use data to trace the background that led to the drop. If you accumulate daily generation data and on-site records and keep them in a state where they can be compared, you will improve early detection of anomalies and the accuracy of identifying causes.

To make checking power generation at solar power plants more efficient, a system that allows on-site records, generation data, location information, photos, and inspection histories to be handled without fragmentation is helpful. If you want to determine the causes of low power generation from both data and on-site conditions and reduce management rework, it is best to start by establishing a management system that can centrally organize inspection records and generation data.