Development of a Low-Cost, Open-Source LoRA-based SCADA System for Remote Monitoring of a Hybrid Power System for an Offshore Aquaculture Site in Newfoundland

In this article a low-cost and open-source Internet of Things (IoT) based Supervisory, Control and Data Acquisition (SCADA) system for remote monitoring of the hybrid power system for an offshore aquaculture site is presented. The selected site is situated 2 km away from the coastline where there is no electrical utility infrastructure and limited communication options are available. The hardware of the designed system primarily consists of six field sensors, Arduino Leonardo as Remote Terminal Unit (RTU), LoRA (Long Range) gateway, cables, AC/DC current and voltage supplies. Arduino IDE, AWS, Influx DB, and Grafana provide the software support. The field sensors are responsible for measuring the solar, battery, inverter & generator currents, along with battery voltage and temperature. All of the field sensors except the temperature sensor send the data to RTU which further delivers it to The Things Network (TTN) cloud. With the help of influx DB, AWS cloud computing services, and Grafana, the data can be stored and visualized through interactive yet informative graphs. The graphs display the historical and live data of each sensor. Further, it also gives the option to set alarms and alerts on user-defined conditions to improve control over the hybrid power system. The complete hardware is assembled and tested in Memorial University’s Power lab. The developed system was supplied with variable current/voltage supplies and the data was logged for three continuous hours. However, the data can be stored for a much longer duration as per user’s requirement. The hardware and the results presented here are a testament that the proposed design system is capable of providing a remote monitoring solution for the offshore aquaculture site.

Introduction

The reliance on renewable energy to meet the energy needs of industrial, commercial, and residential installations is increasing each day due to its unparalleled competitiveness on economic and environmental grounds. Amongst available renewable energy resources, solar PV provides great flexibility to be installed over a variety of places i.e., rooftop, ground, and over water bodies. This distinguished convenience attached to solar PV systems is a beacon of hope to bring vast transformation in the global energy mix and has the capability to realize the vision of reduced emissions in coming years. The monitoring of PV power systems is of paramount importance to ensure optimal performance, timely maintenance of the system, and fault diagnosis.

The robust and cost-effective monitoring of PV systems forms an integral part of improving the resilience of the overall system. SCADA system, which is a combination of software and hardware, provides the liberty to supervise, collect, transmit, and log the data [1]. The SCADA system primarily comprises Field Sensors, Remote Terminal Unit (RTU), Communication Equipment/protocols, Human Machine Interface (HMI), and Servers. Sensors act as the eyes and ears of the whole system, have direct interaction with the system to be monitored and data is collected for onward transmission. RTU is like the heart of the system, is essentially a microcontroller, receives the data from field sensors, processes it in meaningful form, and transmits it to the central system. The data from RTU is sent to a central monitoring system/cloud/website using an appropriate communication channel [2]. HMI receives the data and displays it according to the user’s required format. Finally, Servers log the historical data for analysis and decision-making.

As of now, four generations of SCADA have been witnessed and it is continuously evolving considering the industry demands, refer to Fig. 1. Its first generation came to the surface in the 1960s and primarily focused on hardware-based solutions. The second generation adopted the power of emerging software and also improved the graphical user interface to make its usage friendlier. The third generation improved HMI for better data visualization and provided better connectivity with interfacing equipment. Whilst the fourth generation improved its interoperability with other systems, security, and ability to access data remotely.

Fig. 1. Evolution of SCADA.

The open-source SCADA systems offer a unique and competitive offering by combining different low-cost but compatible equipment together to provide similar services as a proprietary system. The cost is always a concern in proprietary SCADA systems and often they are not very welcoming in interfacing with the other manufacturer’s equipment. Amongst the variety of prevalent communication technologies to transmit data from RTUs, LoRA (Long Range) is preferred for long-range communication. Further to this, it is also energy efficient (low power consumption) and offers wide area coverage where conventional technologies like Wi-Fi or cellular service are not feasible.

Literature Review

Solar energy has been adopted as the preferred source around the globe and its vibrant presence can be felt everywhere. The solar installations’ pyramid is achieving new heights every day and now the more important concern is to look for possibilities to improve the performance, efficiency, reliability, and convenience of the system. The implementation of a SCADA system to monitor and record the operational performance of output power and other parameters is certainly an avenue that can bring the above-mentioned attributes to solar installations.

In [3], an IoT-based open-source low-cost SCADA system comprising current/voltage sensors mounted on Arduino UNO, Raspberry Pi2, and EmonCMS is implemented. The sensors took the actual data from PV panels and data was logged for 2 weeks. The sensors send the data to Arduino attached to Raspberry Pi and Node-RED is used to communicate data to EmonCMS. The operator can see the live data and take the required action. A SCADA system was implemented to monitor the electrical parameters (power, voltage, current) of a hybrid system comprising wind, PV, and energy storage systems [4]. The real-time data is monitored using the ThingsSpeak platform. The developed SCADA system also has the capability to interface with MATLAB and low-cost components (Arduino & ATMega2560 RTU) are used. The hybrid system model is developed on MATLAB and its corresponding prototype is implemented; the effectiveness of the SCADA system is verified by comparing the actual results shown on ThingsSpeak with MATLAB simulation. In another research, an open-source SCADA system is implemented to monitor and control the environmental parameters [5]. Based on the environmental parameters, the operation of home appliances is controlled. The atmospheric sensors and light-dependent resistor are connected to ESP32, the local server is made using Raspberry Pi2, and the data is sent over the web using MQTT protocol and Node-Red. In [6], the author implemented an IoT-based application where data is collected using a temperature and humidity sensor, Node-Red acts as a server and data is stored in its internal database. Finally, the data is sent to the IBM Bluemix cloud. The air quality monitoring system is implemented to measure the presence of carbon monoxide, ozone gas, and temperature. ESP8266 is used as RTU, and data is sent using MQTT Broker. Node-Red receives the data and sends it to a web-based application dashboard [7].

A SCADA system is developed for the renewable energy-based microgrid that has the ability to send data to a server using LoRA gateway [8]. The data encryption is implemented with the AES algorithm to ensure data security. The mesh topology was used to enhance the communication range of the LoRA ESP32 gateway and 500 m coverage is increased. The comparative analysis of ESP32 and Dragino-UNO-based gateways is presented, and both are used to upload the data to the server. A very sophisticated and cost-effective alternative is presented in [9], to calculate the sag and temperature of the transmission line by utilizing the vision system. The cameras installed on poles took photos, process them, and sent to the SCADA system using LoRA communication to determine the sag and other transmission line parameters. The data transfer between microgrids is achieved using LoRA communication. The electrical parameters of the individual generation system are collected by the local controllers and then transmitted to a central station using LoRA, the range testing of data transmission is done up to 4 km [10]. Further, the author explained the competitive advantage of LoRA technology and its applications in SCADA [11]. LoRAWAN allows communication over long distances by maintaining low power requirements that can lead to cost-effective and reliable SCADA/IoT applications. It also states that a 2400 mAh battery can provide backup for 4 hours to a device that sends one byte of message after every 30 minutes.

It is quite reasonable to establish that the remote monitoring of the system is inevitable to ensure healthy operation, timely maintenance, avoid downtime, and maintain the efficiency of any power system. In this paper, the LoRA-based low-cost and open-source SCADA is designed and tested considering the actual constraints of the offshore aquaculture site, and the complete setup is explained here in the following sections.

Proposed System Description

A hybrid power system dominated by floating solar PV panels is designed for an offshore aquaculture site located near Red Island, Placentia Bay, North of the Atlantic Ocean, Newfoundland, province of Canada. It has a total of eight cages refer to Fig. 2. The selected site is in the Atlantic Ocean, around 2 km from the land. There is no presence of electrical infrastructure and only limited options of communication technologies are available. Therefore, the long-range based communication setup is selected which can communicate the data to the control room located a few kilometers away on land. The hybrid power system consists of a Diesel Generator (DG) 99 kVA, PV (363 kW), and battery bank (542.1 kWh), the complete details of the designed hybrid power system can be found in [12].

Fig. 2. Project site (Offshore fish farm).

The output of the DG, PV, and battery bank is measured through sensors. Arduino Leonardo is the RTU that collects data from sensors and has the capability to transmit it to TTN through an in-built LoRAWAN module without any additional hardware. To receive the data from RTU at TTN, the LoRA gateway is registered at TTN, and Arduino Leonardo is also registered as an application to the TTN network. Once the data is received on TTN, it has multiple options to send it to the cloud or store it on the local server. Using the MQTT communication protocol, the data is pushed to Influx DB for storage. Amazon Web Services provides virtual cloud computing services (EC2), Telegraf and Grafana are used to receive, process, and display historical and live data through interactive charts. The orientation/schematic of the proposed system is shown in Fig. 3. The scope of this research paper is limited to the development of a prototype in accordance with the actual system and hence it is prepared accordingly. Details of the hardware and software used to compile the presented architecture are explained explicitly in the following sections. Further, the algorithm and flow chart to measure, collect, store, and display the field measurements are explained in Section V of this paper.

Fig. 3. Proposed system’s design block diagram.

Components of the System

The development of low-cost and open-source SCADA system for remote monitoring system can be primarily divided into three following different sections.

1. 1) Field Measurement
2. 2) Receipt and Transmission of field data
3. 3) Graphical User Interface (GUI) development

The components selected to achieve the above-said milestones are explained in this section.

Sensors

Sensors are the front-line warriors for the system and play a pivotal role in the safety and monitoring of the system. The real-time data of the desired parameter is provided by the sensor which provides the basis for subsequent decision/action. In this project, predominantly three types of sensors are used.

1. 1) Current Sensor
2. 2) Voltage Sensor
3. 3) Environmental Sensor

Current Sensor

The currents of PV solar modules, battery banks, inverters, and generators are measured. The solar modules and battery current are essentially required to be measured with a DC current sensor whilst the inverter and generator current needs to be measured with an AC current sensor. ACS712 Hall effect sensor is selected for the prototype development. This sensor can measure AC & DC current and has three different types based on its current measurement rating, 05 A, 20 A & 30 A. For the sake of prototype development, the 05 A sensor is selected. It needs a 5V_DC supply and ground connection to power up. It’s a linear current sensor that makes measurement easier. Further, it also provides isolation from the actual current-carrying conductor and enhances the safety of the sensor and subsequent connected equipment. The sensors required for the actual hybrid system are also worked out here.

PV Modules

There are a total of 72 parallel strings of panels and the short circuit current of each sensor is 9.48 A. The total current of the PV installation shall be 683 A. In order to improve the reliability of the system, we can use four CR5210 DC current sensors, each with has current range of 200A_DC.

Battery Bank

The battery bank consists of 13 parallel strings and each cell is of 105 Ah. One CR5210 sensor can measure the current.

For the inverter and Generator, a CSCA-A Series Hall effect-based sensor manufactured by Honeywell can be used.

Voltage Sensor

The voltage sensor is used to measure the output voltage of the battery bank. A very simple but precise voltage sensor having a range of 0–25 VDC is used for our prototype. It works on the principle of voltage divider and reduces the input voltage with a factor of 5. It is made using two resistors of 30 kΩ & 7 kΩ. The DC bus voltage of the actual system is 360 VDC therefore, CR5310 having a voltage range of 0–600 VDC can be used.

Environmental Sensor

The performance of the PV modules depends greatly on the temperature they are exposed to. With the increase in temperature, the open circuit voltage of the panel decreases and its short circuit increases, refer to Fig. 4.

Fig. 4. Temperature relation with PV performance.

The Things Node which is a smart temperature sensor with an in-built LoRAWAN module is used here [13]. The Things Node is a very friendly device and can be added as an end device to (TTN) under applications. Once it is connected, it monitors the real-time temperature and sends it to the TTN cloud.

Arduino Leonardo

Arduino Leonardo is a very useful, widespread and open-source microcontroller used in developing IoT-based projects. It is from the family of Arduino that offers user-friendly hardware and software for constructing a wide range of applications. The microcontroller used here is also called The Things UNO which is based on Arduino Leonardo added with a Microchip LoRAWAN module and is a product of The Things Network [14]. The product is programmed through IDE, installed on a PC, and can help to take off the measurements provided by the sensors. This powerful microcontroller serves as RTU, taking the field measurements from sensors and sending them to the TTN cloud with the help of the LoRA gateway. The pin layout is shown in Fig. 5, all the sensors are connected on ADC pins (from A0 to A4). The ground and VCC (+5 V) are kept common for all the sensors.

Fig. 5. Pin layout of Arduino leonardo.

LoRA Gateway

There are many communication technologies that exist nowadays but most of them are not energy efficient and also has limitation in coverage area [11]. Low Power Wide Area (LPWA) technologies offer a great solution when it comes to transferring a small amount of data over a long range with minimum power consumption. This is the primary difference of LPWA from other prevalent wireless technologies [15]. The comparison for the range of wireless networks is given in Fig. 6.

Fig. 6. Range comparison of wireless networks.

LoRAWAN is a type of LPWAN network that uses chirp spread spectrum (CSS) modulation that helps it to achieve long communication range with minimum power needs. The Things Network-manufactured LoRA gateway is used here which helps to build a web-based setup. It provides a convenient and robust way to communicate the sensors’ output to the internet [16]. The outdoor gateway is available to function at 868 MHZ (EU) and 915 MHZ (US) and its range is up to 10 km.

Influxdb

InfluxDB is frequently used in a variety of sectors and applications, including IT operations for monitoring and alerting, IoT for collecting and analyzing sensor data, and applications that need analysis of historical data. Both open-source and commercial versions are available, the open-source version is used for this project that stores the data for 30 days. Further, it’s very compatible with other applications and provides stored data as input for smart data visualization.

Amazon Web Services (AWS)

AWS is a globally recognized, popular, and comprehensive cloud computing service provided under the banner of Amazon. It offers a vast range of cloud services including storage and data analytics. It is very adaptive and provides convenient access to a wide range of technologies to build according to requirements. Amazon Elastic Compute Cloud (EC2) which offers the freedom of developing virtual servers in the cloud is used in our project to provide the platform to store and visualize the data coming from TTN.

Grafana

Grafana is a free, open-source analytics and interactive visualization tool that offers graphs, charts, and other visual representations [17]. It is widely used in various industries and domains for monitoring, observability, and data visualization. Users can create customized dashboards by adding multiple panels and visualizations, making it easy to monitor and analyze different aspects of data in one place. Grafana provides alerting capabilities, allowing users to set up alert conditions and receive notifications when certain conditions are met. This is crucial for monitoring and responding to critical events or anomalies.

Algorithm and Flowchart of the Proposed System

The data of the field sensors is sent to RTU and is processed by it to show at the serial monitor of Arduino IDE by following the below-described algorithm.

Algorithm for sensor data reading at Arduino (RTU)

1. 1) Add the appEUI and appKey of RTU from TTN.
2. 2) Define the analog inputs of RTU.
3. 3) Set the floats for resistor values in voltage sensor.
4. 4) Define and set the sensitivity of ACS712.
5. 5) Determine samples of measurement from sensors and take the average.
6. 6) Convert the average value of measurement to the actual value considering the sensitivity of sensor.
7. 7) Print the live measurements at serial monitor.
8. 8) Go to step 5 and repeat.

Algorithm for sending data from RTU to Grafana

TTN

1. 1) Register LoRA gateway and RTU on TTN
2. 2) Set the Payload format at TTN.
3. 3) Create an API key in TTN.
4. 4) Get MQTT Credentials from TTN

Influx DB

1. 1) Sign up for the Influx DB Account.
2. 2) Create the database for TTN Data.
3. 3) Setup Telegraf Config by entering the MQTT Credentials you got from TTN.

AWS EC2 with Grafana and Telegraf

1. 1) Sign up for AWS Free Account
2. 2) Setup an AWS EC2 Instance with Amazon Linux
3. 3) Connect to AWS EC2.
4. 4) Install Docker
5. 5) Install Telegraf and run the Telegraf configuration set in InfluxDB. This should run in the background.
6. 6) In docker, run an image of Grafana Cloud on a container.
7. 7) Access Grafana Cloud by using the EC2 IP and port set to run the container.
8. 8) In Grafana, go to Data Sources and select Influx DB.
9. 9) Choose Flux for your query language.
10. 10) Input the Influx DB Credentials and Database.
11. 11) Create the dashboard in Grafana.
12. 12) In the dashboard using InfluxDB as the data source set up the charts and visualization
13. 13) Go to Dashboard to visualize the data.

The flowchart describing the data transmission and visualization can be seen in Fig. 7.

Fig. 7. Flow chart of complete system.

Implementation Methodology

The implementation of the IoT-based SCADA system starts from the configuration of the LoRa gateway which requires making an account on the Things network and then registering the gateway by providing its unique EUI, ID, and the suitable frequency plan prevalent in your area (we use 915 MHZ). Then the gateway is connected to the internet through local Wi-Fi, it can be connected through ethernet as well. After that, the RTU (Arduino Leonardo) is registered with the TTN under applications. To do that, RTU is connected with HMI (laptop) through USB 2.0, and the device EUI (Extended Unique Identifier) is retrieved through IDE software. The device EUI is used to register the RTU at TTN as the End Device under the applications tab. Then the RTU configuration is verified by using OTAA, once it is done successfully the data packets’ transmission from RTU to TTN cloud is verified. Since the LoRA gateway has a range of up to 10 km we did the range testing as well. The RTU and the Things Node were taken away from the gateway for 3 km, and they were successfully transmitting data at TTN.

There are a total of five field sensors used to measure the electrical parameters of the hybrid power system. Solar current, Battery Current, Generator Current, Inverter Current, and Battery Voltage sensors are connected at RTU ADC pins A0, A1, A2, A3 & A4, respectively. The current sensors are connected in series while the voltage sensor is connected in parallel. For convenience, all the sensors are mounted on a breadboard. The VCC (+5 V) and ground are supplied to all the sensors (ACS712) sensors through the common wires from the RTU’s respective ports. Solar and Battery current sensors are supplied with the DC current source through a shunt resistor having a power rating of 7 W and 2.2 Ω. Whereas the generator and inverter current sensors are supplied the AC current via autotransformer, model no is 3PN1010B. The autotransformer can supply the variable voltage/current, the output voltage can be 0–140 V, and the corresponding current also varies from 0–10 A. The AC current is supplied to sensors via a rheostat of 10 Ω & 300 W of power rating. The voltage sensor which has the capability to measure voltage in the range of 0–25 V is connected in parallel to the fixed DC voltage source. The common ground from the RTU is connected to the voltage sensor and its analog pin is connected to the ADC pin of the RTU. Although the readings taken from sensors can be seen at HMI but to check the accuracy of the developed system, multimeters are connected at several points to take the continuous and live readings of current and voltage of all the sensors to ensure the accuracy of sensors and subsequent deployed system. The complete hardware setup is implemented in Memorial University’s power lab, refer to Fig. 8. The temperature is measured through the Things Node, and it was added as an end device to the TTN cloud similar to the RTU.

Fig. 8. Prototype hardware setup.

The RTU takes the readings from the sensors and reports them to the Arduino IDE software, the readings from the sensors can be seen on the serial monitor of the IDE by following the steps outlined in Section V. Since the RTU is configured as end device as TTN so the live data reported by RTU can be seen at the cloud that can be accessed by the authorized users on internet anywhere in the world. TTN offers a variety of communication protocols under the integration tab (MQTT, Webhooks, etc.) to transmit and visualize the data on several compatible platforms [18]. We took advantage of the remarkable virtual server AWS and combined it with Influx DB and Grafana to store and plot the data. The detailed steps to push, store, and plot the IoT data are outlined in Section V.

The data is logged for three continuous hours, all the sensors were connected with the explained setup and continuously sending data. The sensors send the live readings at IDE after every 40 seconds and the same readings are reported at TTN after one second on average, refer to Fig. 9. The data received at TTN is saved at InfluxDB and 30 days of storage is supported in a free version. InfluxDB offers great flexibility in accessing the periodic data. Further, a graphical user interface is developed on Grafana, and the graphs of each current sensor can be seen under the dashboard. Grafana allows a wide window to see the historical data according to the user’s requirement for analysis and decision-making.

Fig. 9. Raw sensors data received at TTN.

TTN allows to set the payload formatting of the received data as per the user’s requirement. The payload formatting of the sensors’ data received at TTN is set in accordance with the connection of sensors with RTU ADC pins, as defined above in this section, to ensure compliance with RTU serial monitor readings. The raw data of each individual sensor is received at TTN cloud after an average of 40 seconds with its unique name (matching with RTU serial monitor) and can be seen in Fig. 9. The raw data of sensors is stored at InfluxDB and graphical user interface of each sensor is developed on Grafana to show the historical/live data.

Figs. 10 & 11shows the current value and data log for three hours (from 14:00–17:00 hours) of solar and battery current on the Grafana dashboard. The solar and battery current sensors are supposed to measure the DC current output of PV modules and battery bank so to resemble the actual power system scenario, the sensors were connected with the DC power supply through the resistance (particulars discussed above). The y-axis shows the magnitude of the measured current whilst the x-axis shows the time range. Since the selected sensor model can measure a maximum of 05 A, the maximum range of the y-axis is selected accordingly. The average value of solar and battery current calculated in a three-hour duration was around 0.7 A while the peak value was 1.5 A. Some sudden changes in current magnitude (increase/decrease) can be seen in the said figures which were intentional and brought in current inputs to have some visible movements in the graph. Further, the temperature of the connected resistances was continuously monitored, and it was observed that at the peak current (1.5 A), the resistance was getting heated abnormally therefore, the magnitude of the current was reduced immediately to avoid any damage to the resistors.

Fig. 10. Solar current.

Fig. 11. Battery current.

The inverter and generator current sensors’ readings are also logged for three continuous hours, refer to Figs. 12 & 13, respectively. The sensors are connected to the regulated AC power supply (autotransformer, as described above). The average current value recorded during the duration was 2 A while the peak current was 3.5 A. The rheostat was of higher power rating relatively therefore we had the margin to increase the magnitude of currents comparatively. The magnitude of the current was changed periodically to have visible effects in the graph. The temperature of the rheostats was monitored continuously, and it was observed that at a peak current of 3.5 A rheostats get heated abnormally therefore, the current was reduced near to the average value. The battery voltage is measured through the voltage sensor connected in parallel to the DC voltage source and data is logged, refer to Fig. 14. The temperature is monitored by the Things Node which senses the environmental temperature and sends data directly to the TTN cloud without the involvement of RTU. The historical and current temperature is also shown on Grafana, refer to Fig. 15.

Fig. 12. Inverter current.

Fig. 13. Generator current.

Fig. 14. Battery voltage.

Fig. 15. Temperature measurement.

Discussion

The unique offerings and features of the designed system can be summarized as follows.

1. 1) The designed system is capable to collect, transmit, process, storage and display the live/historic data so it is an IoT-based SCADA system.
2. 2) The field sensors take the actual and precise measurements from the respective equipment and transmit it to the RTU, which pushes the raw data to TTN cloud (refer to Fig. 9), using its inbuilt LoRAWAN module. Further, the database of TTN data is created at InfluxDB and Telegraf is configured by entering the MQQT credentials of TTN. AWS account is created to connect it to the elastic compute cloud (EC2) which allows users to run virtual servers. The telegraf is configured with AWS EC2 and Grafana cloud is accessed by using the EC2 IP. Further, the data source is set as “InfluxDB” in Grafana, and under the dashboard the historical and live data of all the sensors can be viewed with multiple options of charts (bar, line, etc.). The guideline for connecting the TTN to InfluxDB and InfluxDB to Grafana can be accessed at [19], [20], respectively.
3. 3) The sensors are measuring and reporting the data to RTU after an average of every 40 seconds which is pushed to Grafana dashboard by following the steps outlined above. The data logging of all the sensors is done for three continuous hours and can be seen in Figs. 9–15.
4. 4) The hybrid power system designed for the offshore aquaculture site to supply the energy needs consists of solar (363 kW), battery backup (542.1 kWh), inverter (100 kVA), and a diesel generator (99 kVA). But the scope of this research paper is to develop a prototype for the remote monitoring of the hybrid system therefore a lab test setup is developed to demonstrate and highlight the required combination of hardware and software to achieve remote monitoring. The SCADA system can be designed and implemented for the actual hybrid power system by following the steps mentioned in this research paper.
5. 5) A low magnitude of current is supplied to sensors to ensure the safety of equipment and continuous operation without any damage.
6. 6) Low-cost and open-source components are used to build the SCADA system therefore, small businesses can also get its benefits and monitor the aquaculture sites remotely.
7. 7) We verified that remote monitoring is conveniently possible, the control room away from the project site (up to 10 km) will be able to acquire and store the live/historical data. Further, authorized users can view the performance data anywhere in the world on TTN and Grafana.
8. 8) The data can be visualized based on the user’s requirement, the designed and deployed system offers a flexible and wide array of options.
9. 9) The data can be stored for a considerable time and if it is required to increase the storage period, can be done at a competitive cost.
10. 10) The Grafana dashboard is very user-friendly and provides the liberty to adjust the viewing of data according to requirements.

Conclusion

The aquaculture project sites mostly happen to be in remote areas around the globe whether they are inland (pond/lake-based) or offshore sites. The Canadian aquaculture industry is dominated by offshore project sites where the fish cages are inside the ocean (from 0.5 km to 5 km from land). The electrical supply from utility and communication infrastructure is very scarce and its approachability always remains a challenge. The development of a low-cost and LoRA-based SCADA system enabling the remote monitoring of an offshore aquaculture site would be a great help to the industry. Aquaculture is a big industry in Canada that has a considerable share in the Canadian economy. The industry is a mix of big and small businesses. Due to the proprietary nature of the SCADA system, it always becomes a hefty cost to deploy it, and maybe big players of the industry can afford it only. The developed system has its unique offering of providing all the requisite features of a SCADA system but at a very low cost than OEM-owned systems. Thus, it will enable the small and big players of the industry to enjoy the benefits of remote monitoring equally.

LoRA-based open-source low-cost SACDA system primarily comprising of filed sensors, RTU (Arduino Leonardo), LoRA gateway, and HMI was developed and tested in the Memorial University’s Power lab. The DC current sensors measure the solar and battery current whereas AC current sensors measure the inverter and generator current installed. The variable current and voltage sources are connected to check the behavior of the system. The temperature is measured using the Things Node. The RTU collects the data from all the sensors and successfully transmits it to the TTN cloud. The data storage and its visualization are also performed with the help of open-source platforms. The live and historic data can be visualized at the Grafana dashboard, and the auto-refresh is enabled which helps to see the current and historic data side by side. The data is stored for 30 days and can be accessed in CSV format.

The designed SCADA system is not only compatible with power system monitoring but can be implemented for the remote monitoring of any industry with minor adjustments. However, it is fully capable and shall be a pinnacle transformation to bring sustainability and improve the operational performance in the offshore aquaculture industry.