Data analysis for effective monitoring of partially shaded residential PV system

Copernicus Institute of Sustainable Development


Data analysis for effective monitoring of partially shaded residential PV systemOdysseas TsafarakisFreiburg, 25/10/2016


Motivation: The Dutch reality

• From 146 MW (2011) in 1.5 GW (Dec 2015) [photon, cbs.nl]

• 70% on rooftops [cbs.nl, 17/12/2015]

• Average Capacity: 3.5 kWp [TelDeZon 2015]

• Rooftops: lot of obstacles which are creating shadows

• Monitoring: Pac meter between inverter and smart meter

• NO GTI ! Only MeteoSat or local stations


• Automatically detect the energy loss due to the expected shadow

• Distinct it from any additional energy loss due to other faults

• Use only Pac - the most common data

Facts of the study:

The studied PV system is compared with a similar one(s)

Obstacle shadows the PV system some hours of the day.

Any error outside of these hours is characterized as a malfunction

Important problem:

Shadow is not pattern due to diffuse Irradiance

Research target

PV1 vs PV3, Monday PV1 vs PV3, Tuesday


• Experimental facility of S.E.A.C. (Solar Energy Application Centre)

• Located in Eindhoven

• Initial purpose: Study the effect of the shadow in different systems

• 3 PV systems, 6 Panels each:1. String inverter

2. Power Optimizers

3. Micro inverters

• A pole was placed and removed during the year in front of every system

• Irradiance Data: Only tilted irradiance

Data source


Presented Case: 2 Panels with Power optimizers1. Bench (benchmarking): non or less shadowed

2. Studied PV: Highly shadowed (pole – morning, roof – afternoon)

1. Define the barrier of the errora)Ransac (random sample consensus)

b)Cluster the inliers (only green) based on x (Bench Power)

c) Calculate the Barrier and it’s linear regression with the Bench power

d)Thus for bench Power (P) acceptable error is: slope*P+ intercept

Data analysis

Power (W) MAE (W) sigma Barrier (W)

13,36 0,55 1,12 2,80

38,17 0,99 1,29 3,56

62,98 1,68 2,04 5,75

87,79 2,20 2,58 7,36

112,60 2,19 2,32 6,83

137,41 2,37 1,94 6,26

162,22 2,76 1,96 6,69

187,03 3,18 2,29 7,75

211,84 3,82 2,57 8,96

236,65 3,05 2,68 8,42

𝑀𝐴𝐸∗ = 𝑚𝑒𝑎𝑛 𝑏𝑒𝑛𝑐ℎ − 𝑠ℎ𝑎𝑑𝑜𝑤𝑒𝑑

𝐵𝑎𝑟𝑟𝑖𝑒𝑟 = 𝑀𝐴𝐸 + 2 ∗ 𝑠𝑖𝑔𝑚𝑎**

* Mean Absolute error**The σ of the MAE.

[Watt

]

PV2 vs PV3

[Watt

]

PV1 vs PV3

Shadow

ed P

V [

Watt

]

Bench PV [Watt]


2. Barriers are applied to all data:

Barrier for every timestamp is calculated

Every measurement is characterized with color:

• If | bench – studied | > Barrier : color = red

• If | bench – studied | < Barrier : color = green

3. Study the concentration of reds on time

Due to diffuse, count only greens with Pbench > min(Pbench)

Time periods with higher red are characterized as shadow

In the example: [9:00 , 12:20] & [19:00 , 19:50]

4. Change the colors inside shadow profile

Reds => Black (shadow)

Greens => Blue (inseide shadow prof – low barrier)

Data analysis


• But what if the pole is not there?

5. Vertical (daily examination)• Different for each shadow profile ([9 to 13] & [19 to 20])

• Daily distinction to shaded and unshaded

• Density clustering (DBSCAN) to categorize the different shadows

• Daily shadow profile(s): first and last day of each group

6. In every daily profile:

again step 3 (shadow profile - hours)

only within hours of shadow profile (+ 20 min)

Data analysis

No available data

Daily profile 1

Daily profile 2

Daily profile 3

pro

f 4pro

f 5D

aily

pro

file 6


1. Define barrier between studied PV systems

2. Find the barrier for every timestamp – red & green

3. Study hourly concentration of errors -> find shadowed hours

4. Study daily concentration of shadows – find shadowed days

5. Find shadow hours only on the shadowed days – But inside the shadowed hours of step 3

Data analysis summary



Thank you for your attention!!Questions – suggestions??

Contact: [email protected]

Data analysis for effective monitoring of partially shaded residential PV system

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