Stage of Potential Identification Irrigation Channel Topography Analysis for Micro-Hydro Power in the Kalibawang Irrigation Primary Channel, Yogyakarta, Indonesia

The research location is along the Kalibawang Primary Irrigation Channel along 25 km, with side tapping intake and water discharge designed at 7 m³/s for irrigation covering an area of 6,337 ha. Kalibawang irrigation water discharge is taken from the Progo River which is systemized from the Progo watershed. Administratively, the Progo Watershed is located in Central Java Province and Special Region of Yogyakarta (DIY). The area of the Progo Watershed is ± 2,421 km², with the length of the main river is ± 138 km. The monthly average discharge of the Progo River was recorded in several places, namely Kali Bawang 58.50 m³/s. At the Kalibawang station, the maximum debit of the Progo River is 331 m³/s and a minimum of 12.00 m³/s (Figure 1).

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Figure 1. The research location Kalibawang Irrigation Channels

Data collection in the form of secondary data (GIS + RBI/ Indonesia Topographic Map), Google earth image data, field data (UAV, TS measurement, measurement of potential discharge with Current Meter/ CM). Data collection methodology for measuring situations applied Nikkon DTM 332 Total Station, flow velocity and potential discharge data. Figure 2 shows that the stages in the study include two main activities, namely identification of discharge data and height difference, then a recommendation for the type of turbine that is most suitable to be applied at the study location. Determination of height difference begins with macro activities through analysis of GIS data, then mezo identification through UAV measurements, and is detailed in micro detail with TS measurements. The GIS and GE data processing stages rely on secondary data in the form of elevation to be calculated into head data. The visualization of the UAV and TS is the result of field data processing, the head value of the UAV stage is the difference in the height of the contour lines resulting from aerial photo data processing, while the head value of the TS can be determined through the measured height and angle data. 

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Figure 2. Schematic model stages for identifying micro-hydro potential through integration of information and technology on irrigation channels

2.1 Macro Data Processing with Google Earth (GE) and Global Information System (GIS)

Secondary data are obtained from Serayu Opak River Basin Area Center (BBWSO) and Geospatial Information Agency (BIG) including irrigation channel shape file and contour data. GIS and RBI are used to determine the elevation by extracting DEM data. The DEM data are created using the ‘topo to raster' method with the input data are in form of the contour data [13]. The DEM data are extracted from the Spatial Analyst Tool, the Extract Value to Points, so the elevation of each point can be known. Each of the elevation point is calculated to determine the potential head based on GIS and RBI using Eq. (1). The irrigation channel segment is determined based on the best potential point that has been selected through a combination of GIS analysis and Google Earth.

2.1.1 Google earth based data processing

Prediction of potential height difference can be calculated roughly with Eq. (1) as follows:

∆H Potential     = Elv a – Elv b      (1)

Or in the following equation:

$\Delta H_{p o t}=\left(\sqrt{\frac{\left(L_{a-b}\right)^{2}}{\Delta H^{2}}} \times\left(\frac{\Delta H}{\sqrt{\frac{\left.\left(L_{a-b}\right)^{2}\right)}{\Delta H^{2}}}}\right)\right)-\left(L_{a-b} \times 10^{-3}\right)$       (2)

$\Delta \mathrm{H}_{p o t}={ }_{\Delta} \mathrm{H}_{a b}-\left(L_{a b} \times 10^{-3}\right)$       (3)

$\Delta \mathrm{H}_{p o t}=\Delta \mathrm{H}_{a b}-\left(\sqrt{L_{a b}^{2}} \times 10^{-3}\right)$       (4)

2.2 Mezo data processing with UAV based data processing

The identification phase, based on GIS as well as Google Earth, have been carried out at the pre-field stage. So, it is efficient to determine potential recommendations which can be used to carry out the topographic and situation measurements and land use using a Phantom 4 UAV drone. Taking aerial photographs UAVs begins with determining the drone's flight path, and desired map scale. The formula used to determine the scale of the map is presented in Eq. (5). The result of this UAV is then processed to produce DEM data which is used to determine the potential for high differences [14].

S = CFL / (Elv H₂ – Elv H₁)       (5)

∆H = Elv H₂ – Elv H₁       (6)

2.2.1 Processing the data and creating DEM

To see the visual contour in 3D form, it is necessary to do the next processing called DEM with surface. The stages in processing UAV data into DEM data and 3D contours are presented in Figure 7. This process is required to clean up irregular and overlapping photo data and generate the georeferenced 3D point clouds [15]. Any software can help to generate the georeferenced 3D dot clouds such as Agisoft PhotoScan, but this software is quite sophisticated in UAV applications and makes it possible to produce DEM and orthophoto in the desired coordinate system. This software uses a lot of computer resources and will require a very fast computer, especially when there are large amounts of data need to be processed [15]. The data processing begins with making Tie points representing the number of 3D cloud points which amount to 86,992 points (see Figure 7b). For geomatic applications, we need firstly to georeferenced the data. One of the methods commonly used for that is direct georeferencing. This method combines the GPS time and camera inertial time, which are important for data adjustment and then generate the 3D georeferenced cloud points [15]. Tie points are generated from 3D point clouds. They, later on, produce 2,473,055 dense clouds (point density 17.2 points/m²) (see Figure 7c). After the dense cloud is generated, the next step is to generate DEM. In this study, the DEM has been produced with a resolution of 24.1 cm / pixel (see Figure 7d). The accuracy of geometric analysis can be seen in the RMS re-projection error. In this study, the value is 0.210641 (3.65088 pix). The end result of this process is Orthomosaic (see Figure 7f).

2.3 Micro data processing with processing the data based on total station (TS)

To have the detailed measurement of the situation, the researcher uses an instrument-Nikkon DTM 332 Total Station, which is aimed at mapping the micro-hydro cascade situation. Measurements with TS are illustrated in Figure 3. The mapped areas include irrigations, river, roads, yards and medium density settlements. Total Station is a combination of electronic theodolite, EDM (Electronic Distance Meter), and data collecting and processing software [16]. The measurement result obtained TS measurement are angle and distance data. The equipment of Total Station instrument used in the measurement includes 2 prisms, 2 poles, static, measuring tape, and the Nikkon DTM 332 Total Station. The researchers have a survey of reference sites, foresight, and back sight. In measuring the land, the researcher uses branching open polygon method. It is downloaded with computer and Trans-IT software. To do the calculation, the researcher uses Microsoft Excel coordinate calculation software and AutoCAD software. They are used to describe the result of long section and cross section measurement. Field measurement locations also include areas for Micro-hydro Cascade infrastructure development plan, including Intakes, Calm Ponds, Penstock Tracks, Aqueduct, Power Houses and Tailrace. The detailed measurements of topographic contours were carried out at point 17 PWF and 16 PWF. The result of height difference result of TS measurements is presented in Table 5, calculated using Eq. (7) [17].

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Figure 3. Observation of height different object

ΔH_ab = L.tan α – h_P + h_TS       (7)

2.4 Processing water discharge data

Determination water discharge is done in two ways: determination of water discharge with secondary data, and determination of water discharge by conducting measurement in the field. Determination of water discharge with secondary data is to determine the water discharge value based on Flow Duration Curve (FDC) using monthly water discharge data within a certain period [18]. FDC indicates the availability of water with the desired probability, so this data are important for estimating hydropower potential [19]. Meanwhile, to determine the water discharge measurements in the field, the researcher uses the Current Meter to measure the flow velocity. Water discharge calculation using Mid-Section Method assumes that the average velocity on each vertical represents the average velocity of each segment so that water discharge water is also in each segment. Then, the sum of water discharge on all segments is the final channel water discharge [20], see Eq. (8).

q_n = v_n [(L_n – L_(n-1))/2] d_n       (8)

2.5 New renewable energy potential (P)

The formula used to calculate the power that can be generated is as follows:

$\mathrm{P}=\rho \times \mathrm{g} \times \mathrm{H} \times \mathrm{Q} \times \eta$       (9)

2.6 Macro data, head identification based on GIS

Indonesia has 3311 weirs and the 1729 weirs are in Yogyakarta. This city also has 1279 Irrigation Area. The selection of GIS data is focused on the potential in the Special Region of Yogyakarta [21]. Based on the GIS map from BBWS Serayu Opak, Kalibawang Irrigation Area which has a large water discharge intake is chosen. Kalibawang is designed with 7,000 liters/second water discharge intake and with Primary channel length of 25 Km.

Based on the Indonesian Topographic Maps (RBI) map from the Geospatial Information Agency, it is found that the location of the study was under a fairly steep hillside. With hilly topographical conditions, it is possible to experience grid-based electricity supply constraints, because it requires greater costs for procurement [8]. Looking at this topographic map, we can know the slope of hillside to determine the height difference roughly. Low slope cannot be used for power plant development though water availability is good [22], this is because there is not enough height difference to flow water swiftly as a turbine drive. The Kalibawang Irrigation Area has an area of 7,152 ha [23]. A prediction of height difference is presented in Table 1 was calculated by Eq. (1). Based practice on the elevation difference and channel length with the slope design of 1‰, the head height.

2.7 Head identification based on Google Earth

In this second stage, aerial photographs are identified through satellite images of Google Earth Pro 2020 to carry out tracking starting from the Irigation Area intake of Kalibawang along the 25 Km-long primary channel.

Based on Figure 4, through Google Earth satellite imagery, it is indicated that there 23 potential points. It is based on the presence of froth (white) on the Kalibawang irrigation channel as an indication of the existence of a Potential Water Flow (PWF). The indication result on Google Earth are crossed rechecking the potential head with the formula in Eq. (1). Based on prediction with Google Earth, there is 23 m of waterfall obtained, and 7 potential points are selected in the primary irrigation channel in Kalibawang Irrigation Area (Figure 5). These potential points are determined based on height consideration by using contours. Then the calculation of the height difference (head) using Eqns. (2-4) is a formula modified by the author. The results show that the three equation modifications produce the same head value (Table 2), while the head value with the equation is roughly calculated so that the value is greater.

The table of analysis result of GIS and RBI height differences is H gross (Macro), so it needs to be detailed with the next step, that is, the UAV-based topographic measurement.

2.8 Mezo data, head identification based on UAV measurement

The results of UAV measurements in the field at each potential point are presented in Figure 6, while the UAV-based high potential differences are presented in Table 3.

Table 1. Head (H) Potential based on GIS and RBI on sta 0+00 until sta 25+00

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Figure 4. Potential identification tracking PWF 23 points form Google Earth Identification

Table 2. Head (H) 7 Potential locations based on Google Earth at sta 0 + 00 to sta 25 + 00

Description number:

1. Number      F. ∆H = Elv_a – Elv_b
2. Sta
3. La-b            G $\Delta \mathrm{H}_{p o t}=\Delta \mathrm{H}_{a b}-\left(\sqrt{L_{a b}^{2}} \times 10^{-3}\right)$
4. mdpl (a)      H. $\Delta \mathrm{H}_{p o t}=\left(\sqrt{\frac{\left(L_{a-b}\right)^{2}}{\Delta H^{2}}} \times\left(\frac{\Delta H}{\sqrt{\frac{\left.\left(L_{a-b}\right)^{2}\right)}{\Delta H^{2}}}}\right)\right)-\left(L_{a-b} \times 10^{-3}\right)$
5. mdpl (b)      I. Potential Water Flow (PWF)  J. Potential Location

Source: The result of data processing (2020)

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Figure 5. Potential identification tracking 7 points PWF priority selected

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Figure 6. UAV measurement location: (a) potential 1, (b) potential 2 or PWF 7, (c) potential 3 or PWF 9, (d) potential 4 or PWF 12, (e) potential 5 (PWF 17), (f) potential 6 (PWF 18), (g) potential 7 or PWF 23

Table 3. UAV topography contour with head potential (DH)

The contour data generated from UAV in the form of water level (above and below the waterfall) are used to determine the height difference (head). Point 1 has a height difference of 15 m according to the existing condition in the development of micro-hydro. PWF 7 has a height difference (∆H) 1.5 m, PWF 9 ∆H 5 m, PWF 12 ∆H 3 m, PWF 17 ∆H 7 m and PWF 18 ∆H 8 m, and PWF 23 ∆H 3.5 m. The UAV reveals that the greatest potential is the Cascade MHPP Potential by combining the PWF 17 and PWF 18 with ∆Hgross 15 m is MHPP Cascade (Figure 7). After the having the best potential location, the next step is making more detail and precise measurements using the Total Station Aircraft (TS). The combination of UAV and TS can produce DEM data with high resolution (Figure 7 a-g). [24].

2.9 Micro data, Head identification based on Total Station (TS) measurement

The measurement result with TS produce a more detailed and precise Topographic Contour (Figure 8) with a result of 12 m high potential difference.

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Figure 7. The stages of processing UAV into DEM (a) flight area and camera location, (b) Tie points, (c) Dense cloud, (d) DEM with surface 2.5 D model, (e) DEM 2D, (f) Orthomosaic, (g) 3D Visualization with terrain color

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Figure 8. Contour topographic measurements with TS (PWF 17 & PWF 18 Cascade Potential)

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Figure 9. (a) Flow Duration Curve (FDC) potentials of irrigation MHPP in Kalibawang (Secondary Data: Q intake 11 tahun 2009-2019), (b) Instant water discharge intake with Current Meter (Primary data)

2.10 Discharge data based on FDC and CM data

Based on the above measurement, the design head on potential Cascade PWF 17 and 6 ∆H is 12 m. There is a difference in the measurement result of height difference between using UAV and TS. The results of measurement of height difference using UAVs are slightly greater when compared to the field result using TS [25]. After getting the value of ∆H, the researcher needs to get the value of P by determining the channel water discharge. Based on the Figure 9.a, the water discharge design of MHPP Cascade option 1 with 85% probability is 5 m³/s, and option 2 with a 75% probability is 5.5 m³/s with production assumption throughout the year including 1 month maintenance in a 1 year. During this study, discharge measurements have been made at the potential location of MHPP in Kalibawang Irrigation. The measurements are carried out from January 2020 to February 2020. The measurements are carried out by the using Mid-Section Area Method with the Current meter (Figure 9.b). Water discharge measurement result in that period shows that the average water discharge of the Kalibawang Irrigation Primary Channel in January 2020 is Q-moment 5.8 m³/s, February 2020 Q-moment 6 m³/s, and June 2020 Q-moment 5.9 m³/s. Based on the analysis from FDC and CM, it is obtained, from FDC a reliable water discharge with 85% probability of 5 m³/s and probability of 75% discharge (Q) 5.5 m³/s.

GIS and GE-based identification is very useful in finding potential locations based on visual waterfalls, and land use. Around those potential areas, it is found 23 potential loads. In GIS and GE (G) and head (H) potency analysis, it is not yet precise. It is still Macro. The best results which finally obtained are 7 locations to conduct the identification of the second stage based on UAV. This technology can minimize the risk during the survey. It is also more efficient and effective and very fast in the field. The UAV result knows the actual condition in the field, the situation and conditions in the field. It is very good for knowing the location of infrastructure placement, penstock pipeline, power house and tail race. In the measurement of the 7 locations it is found a very good location that is the merging of 2 waterfalls (PWF 17 Blumbang and PWF 18 Kedungrong), micro-hydro cascade with potential Head (H) 15 m. Based on the analysis result of momentary water discharge data with CM and the analysis of 10-years data, it is obtained FDC with a probability of 75% with water discharge of 5.5 m³/s. Contour and topography for the height difference from UAV result is not accurate, it is necessary to measure precisely and accurately by using a Total Station (TS) at Micro-hydro Cascade location. After measuring it the height difference is corrected to 12 m. Measurement with TS in this study is used as a reference in the construction of excavation and embankment infrastructure and micro-hydro infrastructure design. The results of measurement is with a height difference (∆H) 12 m and discharge (Q) 5.5 m³/s, the power generated (P) = 500 kW or 0.5 MW. Based on the calculation of the height difference and flow rate, the recommended type of turbine to generate new micro-hydro is Kaplan Horizontal S-Type. The findings of the three classes classification, namely Macro Class, Mezo Class and Micro Class, provide an efficient and effective choice for surveyors in selecting and identifying potentials according to the need for a macro-stage preliminary survey, identified by GE and GIS, feasibility studies with UAVs and engineering details design using TS.

The limitation of this study is only for the open channel of primary irrigation and the channel is not covered by leaf canopy. The next study direction is Potential Water Fall (PWF) as seen from aerial photos, both Google Earth and UAV aerial photos. The appearance of water foam can be measured the width and length of the foam, this information can be investigated in the future whether there is a correlation or relationship between the width and length of the water foam at each PWF with the potential energy or ∆H potential. Based on the results of the total number of ∆H on the PWF GE + formula, the ∆H is 29.5 m, while the UAV measurement is H 28 m. So that the ∆H measurement results from GE and UAV almost have the same total ∆H, so it is necessary to do further research on how to minimize the ∆H gap between GE and UAV, with research carried out on many irrigation systems.