Plant Control System Research Articles

Pathogens of Plant Diseases in Osh City and Their Connection with Climatic Conditions of Kyrgyzstan

The article discusses the issues of improving the environmental situation, creating a favorable environment for health and, therefore, the need to combat plant pathology in large cities of Kyrgyzstan. Methods of increasing sustainability are analyzed and recommendations for improving the efficiency of the urban green space system are proposed. Based on this, the purpose of the work is to study the possibilities of improving the ecological condition of cities in modern conditions by controlling pests of urban plants. During the research, methods of qualitative identification and analysis of pests of urban plants were used. The information and empirical base of the study was our own laboratory research data. The scientific novelty lies in the identification and identification of pests of planted woody, shrubby and ornamental plants in Osh city. Based on the analysis of pests of planted woody, shrubby and ornamental plants in the city of Osh, it is proposed to strengthen control and research in the field of phytopathology to improve the urban environment by greening urban areas with resistant species to the identified pathologies of woody, shrubby and ornamental plant species. The necessity of forming a pest control system for plants and protecting the ecology of cities is substantiated. As a result of the study, the main types of pests of planted woody, shrubby and ornamental plants in urban areas of Osh were identified.

Development of optimization control system for chiller plant based on a predictive model with Multi Stack LSTM and Deep Learning Neural Network Multi Output

Net-zero emissions become necessary for every country, implementing energy efficiency is the way to achieve it. Released report by the International Energy Agency in September 2022 stated that energy efficiency and electrification are the main priorities for Indonesia in achieving NZE. Currently, more than 50 % of energy consumption in buildings is intended for cooling systems (chillers plants). Thus, energy efficiency in buildings cooling systems has great potential to help achieve NZE and support SDGs. This research is intended to find an optimization control system for chiller plant in order to reduce energy consumption of cooling system in building. The development of this optimization control system is based on predictive model with Multi Stack LSTM and Deep Learning Neural Network. This research developed four different optimization control system frameworks. Every developed framework then will be tested by carrying out simulation with Chiller Plant model. The important parameter was found by using correlation matrix. Based on the correlation matrix, it was found that the most influence parameter that affect chiller plant performance are Tcws and Twb. It leads developed Frameworks 1 become the best compared with others developed frameworks in this research, because it used Twb as a variable input in parallel with performance prediction. This developed framework has deficient error MAE, MSE, RMSE respectively 0.8079, 0.6527, 0.8079 and able to reduce energy consumption by 10.72 %.

Evaluating Methods of Software Bill of Materials Generation to Enhance Nuclear Power Plant Cybersecurity

Instrumentation and control (I&C) systems in nuclear power plants (NPPs) are potential targets of cyberattacks and can prove deleterious for the safety of the NPPs. A Software Bill of Materials (SBOM) provides a detailed list of the various components and their dependencies in software, which helps in vulnerability and risk assessment for cyber hygiene and situational awareness. For an NPP, the process of generating an accurate SBOM report can be complex due to the legacy systems and firmware binaries involved. While most current SBOM tools are focused more on modern internet technology software, this research provides insights and guidelines for an NPP to generate an accurate and efficient SBOM. The paper proposes a new methodology to help NPPs categorize software and use appropriate tools to generate SBOMs for their digital I&C systems.

Features of the information and control system of the car's hybrid power plants

Abstract. Problem. Until now, a number of features of IBS power plants of electric and hybrid cars remain unexplored. The applied IIUS analysis and synthesis methods do not pay enough attention to the multi-criteria nature of the arising optimization tasks. Management adaptation methods to changingexternal operating conditions are insufficiently effective. These circumstances do not allow to fully reveal the potential possibilities of IISD power plants of electric and hybrid cars. This determines the relevance of improvement and development of new methods of modeling and optimization of IIUS control of power plants of electric and hybrid cars based on modern theory of automatic control, vector optimization, neural network and neuro-fuzzy adaptive methodology. A methodology for the construction of IIUS invariant to various power plants of electric and hybrid cars has been developed for operational management of modes according to quality, energy and other criteria. Results. The mathematicalsupport of the "Recovery" mode differs from classical problems of optimal control in that it allows changing the parameters of the control power plant, its model type, limit values for control influences, and phase coordinates. Originality. The core of the IICS GSU software is a knowledge base, in which procedural knowledge is presented in the form of frames that implement the algorithmic support of IICS GSU. The knowledge base has a stratified, hierarchical structure that reflects an integrated graph of the synthesis technology of the ZOU solution. The content of the frames of the knowledge base ensures the search for a solution to the ZOU problem in the state space. Frames contain slots that hold not only specific data but also the names of procedures that process them according to a given algorithm. Some frames contain slots filled with product rules. During the creation of the knowledge base of the research prototype, work was carried out on the unification of frame names, which makes it possible to quickly build the knowledge bases of working prototypes. Knowledge frames are a set of classes developed in a visual programming environment. Practical value. The resulting optimal program is worked out using simulation modeling modules, which include direct modeling of systems in multiple states of operation, and if it satisfies the specified requirements, the resulting data is converted into a file containing optimal control influences, which is stored in the database.

Legacy ICS Cybersecurity Assessment Using Hybrid Threat Modeling—An Oil and Gas Sector Case Study

As security breaches are increasingly widely reported in today’s culture, cybersecurity is gaining attention on a global scale. Threat modeling methods (TMM) are a proactive security practice that is essential for pinpointing risks and limiting their impact. This paper proposes a hybrid threat modeling framework based on system-centric, attacker-centric, and risk-centric approaches to identify threats in Operational Technology (OT) applications. OT is made up of software and hardware used to manage, secure, and control industrial control systems (ICS), and its environments include factories, power plants, oil and gas refineries, and pipelines. To visualize the “big picture” of its infrastructure risk profile and improve understanding of the full attack surface, the proposed framework builds on several threat modeling methodologies: PASTA modeling, STRIDE, and attack tree components. Nevertheless, the continuity and stability of vital infrastructure will continue to depend heavily on legacy equipment. Thus, protecting the availability, security, and safety of industrial environments and vital infrastructure from cyberattacks requires operational technology (OT) cybersecurity. The feasibility of the proposed approach is illustrated with a case study from a real oil and gas production plant control system where numerous significant cyberattacks in recent years have targeted OT networks more frequently as hackers realized the possibility of disruption due to insufficient OT security, particularly for outdated systems. The proposed framework achieved better results in detecting threats and severity in the design of the case study system, helping to increase security and support cybersecurity assessment of legacy control systems.

Structural and Parametric Synthesis of a Hybrid Repetitive Control System for Uncertain Multi-Mode Plant

Article deals with the problem of constructing a hybrid adaptive-robust repetitive control system for one class of a MIMO plants which operating with structural and parametric uncertainty and constant action of an external disturbances. A procedure for structural and parametric synthesis of a control system is proposed. At the structural synthesis stage with the help of hyperstability criterion the authors are developing a decentralized combined controller. After that using the method of continuous models the authors are built a continuous-discrete control system. The main feature of a control loop structure synthesis stage is to ensure the validity of V. M. Popov’s integral inequality. For this purpose, the authors determining the special estimates that guarantee positivity for nonlinear non-stationary part of the system under consideration. At the system’s parametric synthesis stage the authors are use the environment of engineering and technical calculations "Matlab — Simulink" to perform optimization modeling of the continuous and hybrid repetitive control systems. This action is carried out using one of the functional optimization methods — a genetic algorithm. Authors use the criterion of generalized operation for automatic control systems to form the functionality for assessing the quality of the proposed system. At the simulation stage initially for continuous repetitive system it was searched its controller parameters which ensure the minimum value of the specified functional. Then, with a given discretization step of the control loop elements, the hybrid periodic system was optimized in order to improve the quality of its operation.

Integrating reliability analysis into MBSE for FPGA-based safety critical I&C system design in nuclear power plants

Abstract With the widespread application of Field Programmable Gate Array (FPGA) technology in the instrumentation and control (I&C) systems of nuclear power plants (NPPs), its design reliability has become the key to ensuring the subsequent safe operation of NPPs. However, the traditional document-based design method is no longer sufficient to meet the current design needs of complex systems, and there is a lack of research related to the reliability and security analysis of FPGA-based systems. Therefore, the overall goal of this study is to integrate reliability analysis into the model-based systems engineering (MBSE)-based design of FPGA-based I&C systems. Based on the typical application characteristics of the FPGA, a digital design framework for FPGA-based systems is proposed to realize a traceable design process from requirements to implementation. Subsequently, a model-based forward comprehensive reliability analysis method is proposed, which uses a SysML-based mapping mechanism to integrate Failure Mode and Effects Analysis (FMEA) and Fault Tree Analysis (FTA) methods into the design process, so as to comprehensively identify and analyze failure modes from systems to components and optimize and improve system design. Finally, the proposed method is applied to the analog output design of FPGA-based systems. The proposed method can provide guidance for improving the reliability of FPGA-based DI&C systems and provide theoretical basis for the further engineering application.

Exergy analysis of a gas turbine cycle power plant: a case study of power plant in Egypt

This research presents an exergy analysis of a gas turbine power plant situated in Assiut, Egypt, operating under high-temperature conditions. The aim of the study is to assess the performance of the simple gas turbine cycle and identify the sources of thermodynamic inefficiencies using the second law of thermodynamics as a basis for analysis. To accomplish this, a model was developed in EES software utilizing real operational data obtained from the plant's control system. The investigation focused on the impact of varying ambient temperature on the exergy efficiency, exergy destruction, and net power output of the cycle. The results revealed that the combustion chamber accounted for the highest exergy destruction, amounting to 85.22%. This was followed by the compressor at 8.42% and the turbine at 6.36%. The overall energy and exergy efficiencies of the system were determined to be 28.8% and 27.17%, respectively. Furthermore, the study examined the effects of increasing ambient temperature from 0 to 45°C on the system's performance. It was observed that as the temperature rose, the overall exergy efficiency decreased from 27.91 to 26.63%. Simultaneously, the total exergy destruction increased from 126,407 to 138,135 kW. Additionally, the net power output exhibited a decline from 88,084 to 84,051 kW across the same ambient temperature range. These findings highlight the significant influence of ambient temperature on the thermodynamic performance of gas turbine power plants. As temperature rises, a greater amount of exergy is lost, resulting in reduced efficiency and diminished net power output. Therefore, optimizing the design of the combustion chamber is crucial for mitigating the adverse effects of hot weather conditions. The insights obtained from this study can be utilized to enhance the design and operation of gas turbine plants operating in hot climates.

Visualization and Control System for a Wastewater Laboratory Plant with Biological Treatment

This paper presents a novel real-time control and electronic instrumentation system (RTC\andEIS) for a prototype plant that uses activated sludge for wastewater treatment. The main contribution in this research was oriented to work on an experimental design with small dimensions and low cost. This design was carried out using two continuous flow storage tanks in which an identification process was carried out with the MATLAB system identification toolbox to know a mathematical model. The mathematical representation obtained is presented as a continuous and a discrete transfer function. To evaluate the performance of the temperature control and instrumentation system, we implemented a simulation of the closed-loop control system and then implemented it using a DAQ, LabVIEW software, electronic devices and the wastewater treatment plant prototype to compare both results. Finally, the secondary treatment effluent was analyzed in a certified water laboratory to have a detailed analysis compared to the initial influent.

Evaluating the reliability of machine-learning-based predictions used in nuclear power plant instrumentation and control systems

The field of data-driven, neural-network-based machine learning (ML) has seen significant growth, with applications in various information and control systems. Despite promising real-world uses, the reliability of models remains questionable. Conventionally, reliability is assessed based on predictive fidelity, accuracy, and training effectiveness; however, quality developmental procedures and excellent training performance metrics do not guarantee operational reliability. Instead, an ML model's predictive performance depends on the training set's representativeness to the intended operational space. It is known that ML algorithms excel at interpolation but struggle with extrapolation tasks. Anomalies and feature drift can also reduce operational performance. Determining whether a new sample is an interpolation or extrapolation task involves out-of-distribution (OOD) detection for assessing its proximity to the existing training data. Thus, we present a real-time, model-agnostic individual prediction reliability evaluation method called Data Auditing for Reliability Evaluation (DARE) for applying OOD detection to the training dataset. We demonstrate on a feedforward neural network ML-integrated digital twin for predicting fuel centerline temperatures during loss-of-flow transients. DARE acts as a “data supervisor” in determining the model's applicability under different operating conditions. In this manner, we demonstrate how training data can serve as inductive evidence to support the reliability of ML predictions.

Development of Water Quality Control and Monitoring System for Hydroponic Plants with a Cloud-Based System

Development of Water Quality Control and Monitoring System for Hydroponic Plants with a Cloud-Based System

Automatic Control of Polymer Dosage Using Floc Images in Sludge Dewatering Plant

This study introduces a novel method for estimating floc conditions in sludge dewatering plants by employing image analysis and automatic control of polymer dosage. While previous research has focused on drinking water treatment plants, few reports address polymer dosage optimization using image analysis in sludge dewatering plants. The challenge lies in the high sludge dry solids hindering individual floc recognition due to overlap. The study aims to estimate floc conditions by focusing on gap areas between flocs and implementing automatic polymer dosage control accordingly. The proposed method uses images from an Internet Protocol camera and semantic segmentation to identify the floc gap area. For validation of the estimation method, variations of over and under polymer dosage scenarios were investigated and compared with commonly used floc area methods. The findings indicate that the gap area estimation effectively reproduces the theory of polymer cohesion. Automatic polymer dosage control based on this method demonstrates stable operation in both scenarios. Notably, automatic control outperformed manual operation during continuous operation, resulting in a significant reduction in polymer dosage and a notable increase in heating efficiency compared to manual control. This study presents an efficient approach to optimize polymer dosage in sludge dewatering plants, utilizing image analysis for real-time monitoring and control. By focusing on the gap area between flocs, the method enhances accuracy in estimating floc conditions, thereby improving overall dewatering efficiency. The findings highlight the practical benefits of implementing automatic control systems in sludge treatment plants, potentially reducing costs and environmental impact.

SCADA implementation in microhydro power plant control and monitoring systems of screw turbine type

Screw turbine-type micro-hydro power plants are still controlled manually by humans when there is a change in the electrical load on the generator output. If there is a change in the electrical load, the generator output voltage will change, thus affecting the rotation of the turbine being used. In this case, humans are needed to manually regulate the flow of water entering the turbine to maintain the stability of the generator output voltage by controlling the sluice gate. The water discharge entering the screw turbine will rotate the generator through a belt/gearbox pulley transmission, thus producing an electrical voltage at the generator output. To maintain the stability of the generator output voltage, it is necessary to control the water discharge entering the screw turbine. The expected goal of this research is to design a control system for a screw turbine type microhydro power plant to stabilize the generator output voltage and monitor the output of a screw turbine type generator using a SCADA (Supervisory Control, and Data Aquisition) system. The method used is the research and development method, namely the system design stage based on secondary data that has been collected, where the system design includes hardware design and software design, followed by making hardware such as control panels and devices. Software, which includes display design and leader diagrams and testing, is carried out by testing the system on software, hardware, and overall system testing. From the results of the discussion and analysis, the screw turbine type microhydro power plant control design system was created, a load control system consisting of 6 groups and sluice gate opening control that uses an ultrasonic sensor to determine the water level. With the results of controlling the sluice gate at a door opening of 30 mm, the generator voltage is 18.42 Volts DC, DC current is 0.0 Ampere at a load of 0 watts, when the load is 30 watts the generator voltage decreases by 18.40 volts, current is 1.37 Ampere. Through the on-line monitoring system, electrical parameters are obtained which are displayed on the SCADA system by looking in real-time at the history of DC voltage and DC current, DC power, and DC energy generated by the screw turbine-type DC generator. The voltage read is 18. 57 volts, current 1.37 Amperes, DC electrical power is 25.4 watts with a DC light load of 30 watts.

IOT based Smart Plant Monitoring System and Control System

The world is now fully dependent on the internet and the Internet of Things (IoT), which is the inter-networking of physical devices embedded with electronics, sensors, software and network connectivity. IoT enables physical devices to exchange data with the manufacturer and achieve greater value and services. Agriculture is the backbone of many countries, but water scarcity is a major issue in this field. To overcome this problem, the irrigation process can be automated using IoT. By using sensors, the temperature, humidity, and light can be measured, and appropriate processing can be performed based on the outcome. This helps in reducing water wastage and improving accuracy, economic benefits, and efficiency while reducing human intervention. This paper highlights the importance of IoT in day-to-day life, its scope in the future, and its potential applications in farming methods. To demonstrate the use of IoT in agriculture, we present the Automatic Watering System, which monitors and maintains the moisture content in the soil using temperature, humidity, and moisture sensors. The Arduino UNO microcontroller is used as the control unit, regulating the appropriate amount of water to avoid over/under irrigation. Keywords – sensors, temperature, humidity, soil moisture, Arduino uno

A Fault Diagnosis Method for Rectifier-Filter Circuit Integrating EEMD Algorithm and Transformer Network

Rectifier-filter circuit, as a critical component of the drive circuit in instrumentation and control systems of nuclear power plants, can convert the 50 Hz AC into the smooth DC. Thus, it plays a vital role in the power control of reactors. However, the weak waveform anomalies of soft faults in the rectifier-filter circuit make fault feature extraction difficult. Therefore, in this article, an ensemble empirical modal decomposition (EEMD) algorithm is employed to decompose the signal mode components in the monitor data of the rectifier-filter circuit. The weak waveform anomalies are indirectly enhanced by the IMF and residual components. Subsequently, the Transformer network is utilized to construct the feature extractor. With the advantage of multi-head attention (MHA) mechanism in the Transformer network, the multi-directional, multi-scale, and highly sensitive long-range time-dependent features in the EEMD feature data are extracted. Then, a deep Softmax classifier is adopted to reduce the dimensionality and diagnose the soft faults of the rectifier-filter circuit. Finally, a fault simulation model of the rectifier-filter circuit is constructed and the condition monitor data are collected. The effectiveness and diagnosis accuracy of the proposed method are verified by a real case experiment and some comparative methods.

Study of operating modes of the AION tanker electrical power system

THE PURPOSE. Consider the issue of automation of modern sea vessels with a high degree of electrification. The purpose of the study is to analyze the electrical power system of the AION tanker to determine the possible occurrence of abnormal phenomena during parallel operation of generator sets and methods for eliminating them. METHODS. The article analyzes the electrical power system and automatic control devices of the AION tanker. Particular attention is paid to the study of systems that ensure the production and distribution of electrical energy between consumers. The widespread use of modern computer-based automatic control systems in ship power plants has been noted. The extensive possibilities for controlling a ship's electrical power plant in various daily or emergency situations are shown. The software and hardware of the ship's Power Management System allows you to reduce the consumption of fuel, oil and other important resources by automatically regulating the load on diesel generators, timing the supply of fuel to the cylinders, and controlling exhaust and starting valves. RESULTS. Experimental oscillograms of studies of the parallel operation of diesel generator sets were obtained, in which power exchange oscillations were recorded. The amplitude of power exchange oscillations reaches 40 % of the steady-state value, and their period is 600 - 800 ms, depending on the operating mode of the ship's electrical power system. During thecollection of these oscillograms, the ship's load is turned on and no starts or stops of any powerful consumers are made, that is, a quasi-steady process takes place. CONCLUSION. It is proposed to introduce a function for eliminating power exchange oscillations into the ship power plant control system. To implement it, it is necessary to add a block that receives information from all parallel operating diesel generator sets and adaptively adjusts the settings of the frequency regulators.

Novel Approach for Network Data Exchange Among Safety-Related Control Systems of Nuclear Reactors

Industrially mature digital networking technologies form the backbone of the distributed control architectures of nuclear reactors. Typically, hundreds of control systems responsible for assuring safer and secure plant operations are located in the field and interconnected via wired isolated local area networks, which are also connected to centralized servers and operating consoles located in the main control room. Many of such control systems require data processed by other systems situated far away in the plant, which mandates multiple concurrent reliable TCP sessions to be active or live for such field-level control systems. Multi-socket communication beyond 10 is practically difficult for embedded systems with limited memory and processing capabilities; hence, data accumulation, extraction, and routing are typically done with the help of commercially available high-performance servers and switches. The servers and operating consoles of a plant are generally Linux-based commercial-off-the-shelf (COTS) industrial computers, whose development lifecycle is not accessible for review and in accordance with safety guides; hence, qualifying such components for safety operations is extremely difficult. This paper presents a novel way of independently offloading safety-related data transactions from such COTS components (reducing dependency) with the help of very simple, reliable, and secure hardware, which is referred to as a data exchange and processing unit (DEPU). An approach is proposed to realize distributed control architecture with this proposed hardware (to simplify qualification tasks) along with existing servers and operating consoles without compromising powerful and convenient human-machine interface as well as the data logging capabilities of such components. Hardware and software design perspectives, performance characterization, and qualification testing of the DEPU in accordance with nuclear power plant instrumentation and control system design guidelines are also briefed in this paper.

Verification and validation optimization method for signal quality bits in digital control system application software of nuclear power plant

Abstract The digitalization enhances nuclear power plant (NPP) instrumentation and control (I&C) system performance, while simultaneously introducing safety and reliability challenges. To enhance the stability of distributed control system (DCS), and minimize the dangerous failure rate and safe failure rate of safety system, signal quality bits (SQBs) are widely used in NPP DCS to identify the validity of important signals. However, in the past decade or so, numerous unscheduled turbine shutdowns and/or reactor trips have resulted from incorrect SQB design or settings. Therefore, it is necessary to conduct optimization research on the design methods and setting principles of SQBs, establish an optimization method for DCS application software SQB verification and validation (V&V), then minimize DCS faults attributed to SQB. With the aim, this work conducts an in-depth investigation and analysis of the definition, design, setting and transmission of SQB in NPP. A specific optimization method for software V&V to conduct SQB on NPP safety DCS application software is proposed. Twelve concerns in the SQB V&V work are detailed. The reactor trip system ΔT protection logic is utilized as a case to illustrate the detail implementation process of this optimization method. This work provides an engineering technical reference and operational guidance for SQB software V&V, conducive to ensuring the safe and economical operation of NPPs.

Моделирование работы газотурбинной установки SGT5-4000F с исследованием влияния режимных и климатических факторов на устойчивость горения в камере сгорания

When regulating gas turbine units, it is necessary to be confident in the stable combustion process in the combustion chambers, considering the dynamics of the processes and changes of external climatic factors. Many scientific studies are devoted to the problems of experimental research and mathematical modeling of processes in combustion chambers. The possibilities of flame failure, expansion of the lower limit of combustion, determination of the stable position of the flame front and other issues have been assessed. Even though the problem of design of mathematical models and their use remains relevant. The purpose of this research is to model and study the influence of operation and climatic factors on the stability of combustion in the combustion chamber of the SGT5-4000F gas turbine unit. The subject of the research is the SGT5-4000F gas turbine unit with a low-emission combustion chamber. The simulation model of the gas turbine unit has been developed in the environment of dynamic modeling of technical systems SimInTech. The research is carried out using theoretical methods of calculating fuel combustion, thermodynamic foundations of the theory of gas turbine engines, methods of mathematical and simulation modeling, as well as data from the archive of automated process control systems. A simulation model of SGT5-4000F gas turbine unit has been developed. It is distinguished by the ability to assess the proximity of the compressor operating mode to the stability limit and determine the boundaries of stable combustion procedure in the combustion chamber, considering environmental climatic factors. The authors have studied the stability of the compressor and combustion chamber. The results of the experimental studies of a gas turbine unit in the operating load range from 113 to 282 MW and in the range of outside air temperature from –12 to 30 °C have shown that the combustion process in the combustion chamber is unvarying over the entire operational stress spectrum. The results obtained are verified by comparing model values with technological parameters of a gas turbine unit taken from the archive of the power plant control system. The authors have assessed the influence of electrical load on the performance indicators of a gas turbine unit at outdoor temperatures of +30, +15 and –12 °C. According to the results of simulation modeling, the model of SGT5-4000F gas turbine unit is found to be adequate. The developed model can be used as a tool at the stage of functional and technological design of automated control systems for the development of effective automatic control systems.

An Experimental Investigation of Various Control Systems for an Archimedes Screw Turbine in a Micro-Hydropower Plant

The control system for a micro-hydropower plant using an Archimedes screw turbine is the focus of this work. Three control systems were implemented based on a Barreda micro-hydropower plant (Spain) currently in operation: an optimal water level control (OWLC) system, a maximum power point monitoring (MPPT) system, and a water level control (WLC) system. The comparison was made using several assessment indicators: electricity production, micro-hydropower plant efficiency, and gearbox fatigue. The electricity production is similar in the OWLC and MPPT systems (energy gain +0.5%) and significantly lower in the WLC system (energy gain −12%). The efficiency of the micro-hydro plant is similar in the OWLC and MPPT systems (average efficiency gain +0.9%) and significantly lower in the WLC system (average efficiency gain −15%). The mechanical stress on the gearbox is similar in the OWLC and WLC systems and significantly higher in the MPPT system. It can be concluded that the OWLC system performs better as concerns the three assessment indicators used, followed by the MPPT system. The WLC system is not recommended for use, due to its low electricity production and low efficiency of the micro-hydropower plant.