Commercial PV System Data Monitoring, Part One: Page 9 of 11

Degrees of Granularity

PV systems can be thought of as a network of subsystems, much like a river. Just as you could not determine the volume of water in a tributary feeding into the Mississippi River by measuring the total volume of water pouring into the Gulf of Mexico, you cannot determine individual PV component performance by looking at only the ac output at the end of the system.

In a PV system, power is combined at module, string, combiner box, subarray and inverter levels. It is possible to collect power data at each of these points. Higher levels of data granularity provide a more complete look at total system performance. The further a PV system is divided, the more available data there is for benchmark comparisons and troubleshooting.

Unlike a river, the electrical design for PV power systems is generally characterized by system symmetry—by the repetition of similar, if not identical, subsystems. This principal of symmetry is useful when determining the optimal degree of monitoring for a PV system.

To the extent that a PV system design is electrically symmetrical, it may be possible to reduce the amount of granularity needed to adequately monitor the system. For troubleshooting or O&M purposes, manually or automatically comparing the power output at equivalent collection points in a PV system can provide a level of functionality that is similar to installing a more complex and granular monitoring system.

For example, if a PV system designer plans to use 10 sourcecircuit combiner boxes to aggregate power into a single central inverter, each with an identical number of string inputs, then all 10 PV output-power circuit conductors should essentially carry the same amount of power when averaged across a period of minutes. Therefore, if the output of these combiner boxes is monitored, then a combiner box with lower-than-expected power output can be spotted at a glance by comparing the power curves taken at each combiner.

Assuming a symmetrical electrical design, source-circuit combiner box-level monitoring can provide string-level insight into system performance at a fraction of the cost and complexity of string-level monitoring. While monitoring at the combiner box level would not identify exactly which source circuit is not contributing, it can identify that a string has failed and, in this example, target the troubleshooting activities required to fix the problem to just 10% of the array.

Similarly, string-level monitoring can be used to provide module-level insight. In fact, monitoring pairs of ungrounded current-carrying source-circuit conductors may effectively achieve this level of granularity at a relatively reduced cost. The challenge for system integrators is determining at what point additional granularity no longer justifies additional costs.

“Deciding between different levels of monitoring generally breaks down into a cost versus granularity of data argument,” explains Locus Energy’s De Luca. “String-level monitoring enables operators to spot a given string that may be performing below spec, but installing source-circuit combiner boxes with string-level monitoring capabilities is often prohibitively expensive.”

There are, of course, always exceptions. As the value of the data being collected increases, higher degrees of granularity may be justified. In some markets the combined value of solar renewable energy credits (SRECs) and the rate of the energy being offset may warrant an investment in a very granular monitoring system. For example, if every PV-generated kilowatthour is valued at \$0.50, then there is a significant incentive to optimize plant performance, as will be shown in a case study appearing in Part Two of this article.

According to Power-One’s Tansy, “Performance monitoring at higher levels of granularity enables the operator to fine-tune plant performance at a more granular level.” In addition to enabling detailed performance variance comparisons, it may also speed root-cause resolution. The faster and more accurately a problem can be identified, the faster it can be resolved.

Because the cost of data monitoring is proportional to the number of plant metrics collected and the granularity of the data, system owners want to optimize data monitoring, not just maximize it. Determining what constitutes excess data collected at too high a cost and what level of data collection provides the best return on investment is an installation- and project-specific exercise. Just keep in mind that the data you fail to collect today may turn out to be priceless tomorrow.

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