Unit Of Output Research Articles

Thermal performance of shell-and-tube polymeric hollow fiber heat exchangers as oil coolers

Motor oil must be appropriately treated in motors, and aluminum plate heat exchangers are used to cool it. An aluminum heat exchanger is an additional part of the oil treatment module, which increases its size and weight. Two compact plastic shell-and-tube heat exchangers were tested alongside with aluminum heat exchanger to evaluate the efficiency of the plastic cooling core inside the oil module. A total of 63 experimental points were tested on three heat exchangers with good thermal balance (discrepancy of about 1,8 %), and the data were used to evaluate the heat transfer coefficients. While plastic unit PA11 showed the heat outputs exceeding the heat output of the aluminum plate unit by about 7 %, the pressure drops were about ten times higher. The unit PEEK showed good performance and pressure drops on the oil side, but the heat transfer was limited by a small heat transfer surface. Analysis of thermal resistances confirmed that the thermal resistance between the wall and the oil is dominant due to the oil's low thermal conductivity and high viscosity. The results showed that optimization of the fiber structures is needed, aiming to increase the oil flow around the fiber structures.

Recovering Low-Grade Heat from Flue Gas in a Coal-Fired Thermal Power Unit

To achieve the goals of carbon peaking and carbon neutrality, the retrofitting of existing coal-fired power plants is crucial to achieving energy-saving and emission reduction goals. A conventional recovery system of waste heat typically occurs downstream of the air preheater, where the energy quality in flue gas is low, resulting in limited coal-saving benefits. This study proposes a scheme involving a flue gas exchanger bypassing the air preheater and low-temperature economizers, which is used to transfer the waste heat from flue gas to primary and secondary air (System I). Additionally, a heat pump can be introduced to provide supplementary energy for primary and secondary air, as well as the condensate from the steam turbine (System II). The coal consumption rate and exergy efficiency are used to evaluate the two schemes. The results show that both waste heat recovery systems can increase the power output of the coal-fired unit by recovering waste heat. System II can boost power output by approximately 13.98 MW. The power increase in both waste heat recovery systems show a declining trend as the unit load decreases. This increased power is primarily attributed to the medium- and low-pressure cylinders, while the contributions from ultra-high-pressure and high-pressure cylinders are negligible. The increased power output for the medium-pressure cylinder ranges from approximately 3.49 to 3.58 MW, while the low-pressure cylinder has an increased power output of around 10.10 to 10.19 MW. The coal consumption rate is decreased from 250.3 g/(kW·h) to 247.5 g/(kW·h) under a full load condition for both systems, which can be augmented at lower load conditions. System II outperforms System I at 30% load condition, achieving a reduced coal consumption rate of 3.36 g/(kW·h). System I has an exergy efficiency of 40%, while System II shows a higher efficiency of 44%.

Analysis of the dynamics of development of greenhouse vegetables in Russia during the pre-sanctions and sanctions periods

This article examines the dynamics of acreage, the main elements of production costs, labor costs per unit of production, output and profitability of products as key indicators of the economic development of greenhouse vegetables. The analysis of causal relationships of changes and interpretation of indicators of economic development of the sub-sector is carried out. The main challenges and prospects for the development of greenhouse vegetables industry have been studied, including the dependence of the industry on foreign supplies of components and seeds, the need to expand the range of products and strengthen the export potential of the industry. The results of the study allowed us to conclude that the vegetable growing of greenhouses (horticulture) in Russia has made a significant leap in its development, over the period 2006–2022 significantly increased production volumes and, in general, judging by key economic indicators of development, overcomes sanctions pressure, expressed primarily in the rise in the cost of production and sales logistics.

Decentralised by-product valorisation in the dairy value chain: An opportunity for sustainable intensification

Increasing the sustainability of the livestock sector is a vital goal in lowering the environmental burden of food production globally. The dairy sector is of particular interest in this context since it is responsible for a significant share of this burden. In the past, research has focused on approaches to lower the impacts at the farming stage of production, while fewer efforts have been directed at the later stages of the value chain. By-product valorisation is a tool within the wider framework of Circular Economy that allows to increase the efficiency of resource use by increasing the product output at high quality, potentially leading to a sustainable intensification. The present study aims to test this hypothesis. For this purpose, data was collected from a cheese production case study and pilot-scale experiments on whey valorisation, to model several scenarios of by-product valorisation in the dairy value chain. This includes a decentralised valorisation scenario modelled for the purpose of supporting a local Circular Economy and three scenarios representing common valorisation approaches. The impacts of the entire value chain are then assessed based on an attributional LCA considering three different functional units in order to examine the potential for a relative reduction of impacts per unit of product output. The results suggest that while there is no clear favourite among the investigated valorisation scenarios, by-product valorisation in general can lower the relative impacts of the value chain significantly as compared to the base scenario without valorisation. Depending on the valorisation scenario and impact category, impact reductions of up to 14%, 20% and 32% can be achieved for the three functional units, respectively. Hence, by-product valorisation allows for a sustainable intensification. While centralised lower quality valorisation is favourable in terms of product mass and overall process efficiency, decentralised and high-quality valorisation performs better when the economic value of the products is taken into consideration. Thus, the choice for the most suitable approach for a production site will depend on the context of the production and the intentions of the stakeholders.

Multi-Objetive Dispatching in Multi-Area Power Systems Using the Fuzzy Satisficing Method

The traditional mathematical models for solving the economic dispatch problem at the generation level primarily focus on minimizing overall operational costs while ensuring demand is met across various periods. However, contemporary power systems integrate a diverse mix of generators from both conventional and renewable energy sources, contributing to economically efficient energy production and playing a pivotal role in reducing greenhouse gas emissions. As the complexity of power systems increases, the scope of economic dispatch must expand to address demand across multiple regions, incorporating a range of objective functions that optimize energy resource utilization, reduce costs, and achieve superior economic and technical outcomes. This paper, therefore, proposes an advanced optimization model designed to determine the hourly power output of various generation units distributed across multiple areas within the power system. The model satisfies the dual objective functions and adheres to stringent technical constraints, effectively framing the problem as a nonlinear programming challenge. Furthermore, an in-depth analysis of the resulting and exchanged energy quantities demonstrates that the model guarantees the hourly demand. Significantly, the system’s efficiency can be further enhanced by increasing the capacity of the interconnection links between areas, thereby generating additional savings that can be reinvested into expanding the links’ capacity. Moreover, the multi-objective model excels not only in meeting the proposed objective functions but also in optimizing energy exchange across the system. This optimization is applicable to various types of energy, including thermal and renewable sources, even those characterized by uncertainty in their primary resources. The model’s ability to effectively manage such uncertainties underscores its robustness, instilling confidence in its applicability and reliability across diverse energy scenarios. This adaptability makes the model a significant contribution to the field, offering a sophisticated tool for optimizing multi-area power systems in a way that balances economic, technical, and environmental considerations.

Impacts of Plug-in Electric Vehicles and Renewable Energy Source on Unit Commitment Problem using Cat Mouse Based Optimization A lgorithm

Objectives: The main objective of this paper is to solve the Cost Based Unit Commitment (CBIC) problem considering Renewable Energy Sources (RES) and Plug-in Electric vehicles (PEVs). The proposed problem minimizes the total operating of the interconnected system. Methods: A novel and recently developed successful optimization tool of Cat and Mouse Based Optimizer (CMBO) is proposed for optimal solution of Cost Based UC problem. The proposed CMBO approach is having two groups such as Cat group and Mice group for searching the global optimal solution with less computational time. It contains two phases like Cat’s movement towards Mice and Mice escape to a safe place is effectively updating the new position of Cat and Mice to easily reaching the best solutions. The control parameter of CMBO is number of agents is 50, number of variables is 10 and maximum number of iteration is 500. MATLAB 14.0 platform is utilized for the projected problem. Findings: The method tested on standard IEEE-39 bus system (10 generators and 24 hour) with an equivalent PEV and Wind farm. The CMBO approach minimize the total operating cost with various test cases. The outcomes such as UC schedule, Real power output of thermal, wind and PEV units, fuel cost, startup cost and total operating cost of the interconnected system are numerically and graphically reported. The total operating cost is 4.11 % minimized when compared with base case approach and 0.73 % reduced than the other existing method viz. Parallel UCs-RP method. Novelty: The CMBO is recently developed powerful optimizer and parameter free algorithm, so easily reaches the global optimal solutions. The obtained simulated results are compared with other mathematical and intelligent computational approaches for proving the efficiency and performance of the proposed CNBO technique. Keywords: Unit Commitment, Plug­in Electric Vehicles, Renewable Energy Sources, Cost minimization, Cat and Mouse Based Optimizer

Optimal operation model of ecological flow of hydropower station based on genetic algorithm and neural network

Amid diverse water demands, the conflict between ecological flow adjustments and power generation flow adjustments in hydropower station water resource dispatching, coupled with an increasing diversity of constraints, complicates the issue of water resource optimization and dispatching. How to scientifically develop feedback regulation mechanisms to promote a virtuous ecological cycle, fine tune existing hydropower station scheduling plans, and achieve multi-objective optimization scheduling is the core issue in researching the comprehensive utilization of water resources. This study establishes a reservoir flow regulation model with the objective functions of maximizing power generation and achieving optimal average ecological protection. The model, constrained by water balance, reservoir capacity, unit output, and unit overflow, was optimized and solved using the genetic algorithm-backpropagation neural network (GA-BPNN) approach. This study establishes a reservoir flow regulation model with the GA-BPNN algorithm was applied in multi-objective optimization to identify an optimal solution that maximizes overall system performance while adhering to ecological flow constraints. The results indicate that the optimized plan has performed effectively in hydropower station operations. It not only satisfies ecological flow requirements, but also significantly enhances power generation efficiency. Specifically, power generation increased by 32,680 kw·h, a 23.85% growth rate. The optimized plan significantly enhances the comprehensive utilization efficiency of water resources. It offers a scientific basis for the rational planning, dispatching and utilization of water resources in hydropower stations and reservoirs.

A Bi-Level Peak Regulation Optimization Model for Power Systems Considering Ramping Capability and Demand Response

In the context of constructing new power systems, the intermittency and volatility of high-penetration renewable generation pose new challenges to the stability and secure operation of power systems. Enhancing the ramping capability of power systems has become a crucial measure for addressing these challenges. Therefore, this paper proposes a bi-level peak regulation optimization model for power systems considering ramping capability and demand response, aiming to mitigate the challenges that the uncertainty and volatility of renewable energy generation impose on power system operations. Firstly, the upper-level model focuses on minimizing the ramping demand caused by the uncertainty, taking into account concerned constraints such as the constraint of price-guided demand response, the constraint of satisfaction with electricity usage patterns, and the constraint of cost satisfaction. By solving the upper-level model, the ramping demand of the power system can be reduced. Secondly, the lower-level model aims to minimize the overall cost of the power system, considering constraints such as power balance constraints, power flow constraints, ramping capability constraints of thermal power units, stepwise ramp rate calculation constraints, and constraints of carbon capture units. Based on the ramping demand obtained by solving the upper-level model, the outputs of the generation units are optimized to reduce operation cost of power systems. Finally, the proposed peak regulation optimization model is verified through simulation based on the IEEE 39-bus system. The results indicate that the proposed model, which incorporates ramping capability and demand response, effectively reduces the comprehensive operational cost of the power system.

Proactive Prevention Strategies for Grid Risk Scenarios Based on Pumped Storage Participation

Given the increasing importance of cascading trip prevention, this study presents the pumped storage characteristics to cooperate with thermal units and further ensure the safety of grid operation. The proposed model comprises two phases: the first phase is the day-ahead decision-making phase, which synergistically optimizes thermal units and pumped storage to improve the flexibility of grid operation; the second phase is the intra-day adjustment phase considering the uncertainty of the wind-photovoltaic and transmission line failure outage to enhance the robustness. Furthermore, this model employs a column and constraint generation algorithm to solve the two-phase, three-layer optimization problem, thereby minimizing the adjustment cost under the riskiest scenarios. The experimental results show that adding pumped storage reduces the total system operating cost by 3.99%, the renewable energy abandonment rate to 0.65%, and the thermal power unit output standard deviation to 74.13 MW.

Design and analysis of a piezoelectric energy harvesting shock absorber for light truck applications

Vehicle suspension vibration is a prevalent form of oscillation, with approximately 22.5 % of automobile energy dissipated through suspension-induced vibration. This phenomenon suggests that recovering the energy dissipated by the suspension system holds significant potential. This paper introduces a novel piezoelectric energy harvesting shock absorber (EHSA) based on non-contact magnetic force for light truck applications. The vibration of the vehicle suspension system is transformed into the rotation of a ball screw, enabling the one-way high-speed output of the rotor through a one-way bearing and planetary gear mechanism. Based on the non-contact magnetic force, the piezoelectric patches generate frequent oscillations, and a novel energy harvesting circuit is designed that efficiently converts the output of multiple piezoelectric units into usable electricity. The damping and energy harvesting characteristics of the EHSA with different external and internal variables are explored. Under sinusoidal excitation of 40 mm amplitude and 0.4 Hz frequency, the EHSA achieves a maximum output power of 7.51 W, with an average output power of 1.89 W. Furthermore, under random action, the EHSA can successfully illuminate 824 LED lights, enabling the temperature and humidity sensor to function normally. These findings indicate that the proposed EHSA can effectively harness vehicle suspension vibration to power onboard self-powered appliances while offering a new design idea for integrating vibration energy harvesters into vehicles.

Stage stochastic incremental data envelopment analysis models and applications

Data envelopment analysis (DEA) is a mathematical programming method that can evaluate the relative efficiency of multiple inputs and multiple outputs of a decision-making unit (DMU). The classical DEA model assumes that inputs and outputs are determined. However, there are some applications where the inputs–outputs are stochastic. In practice, it is important to evaluate stage performance. It is essential to eliminate the effect of preceding stage inputs (outputs) on stage performance in order to accurately assess stage performance. In this paper, we propose stage stochastic incremental DEA models that integrate two different kinds of inputs and outputs. The first kind of model takes into account the assessment of stage efficiency when determinate incremental inputs and stochastic incremental outputs are applied at the beginning and end of the stage. The second kind of model uses stochastic incremental inputs–outputs to evaluate stage efficiency. To verify the efficacy of the suggested models, the first kind of model is applied to assess the stage financing efficiency of 15 energy-saving and environmental protection clean enterprises (ESEPCEs). The second kind of model is applied in assessing the stage investment efficiency of 15 ESEPCEs. The empirical results show that the proposed models not only eliminate the effect of prior performance but also more accurately assess stage efficiency in a stochastic environment.

Conversion of a small size passenger car to hydrogen fueling: simulation of full load performance

Abstract Hydrogen offers a valid choice when considering zero emissions transport. This alternative fuel is well suited for spark ignition (SI) engines and it can be implemented with relatively minor changes in terms of added components. Initial evaluations of feasibility with respect to available space for the fuel tank revealed that it is possible to convert a small size passenger car to hydrogen fueling and maintain a range comparable to the fully electric alternative. Peak power assessment showed that additional boosting was required compared to gasoline operation, close to the limit of compressor surge. As a result, the present study extended the engine speed range so as to evaluate full load performance levels that can be obtained when using hydrogen. 0D/1D simulation was applied for simulating power unit output as well as related control parameters. A dedicated laminar flame speed sub-model was implemented for including fuel chemistry effects. Predicted combustion phasing showed the required changes in ignition timing when switching fuels and revealed that the engine could be operated close to stoichiometry when using hydrogen. A reduction of torque was calculated at low engine speed, even if peak power ratings were practically the same as with gasoline fueling.

An exploration of the concept of constrained improvement in data envelopment analysis

Constrained improvement refers to regulating rivalry between companies in a particular industry by defining a framework or an evaluation mechanism. Such a mechanism results in a more equitable and healthy competitive environment. The primary motivation is that the best-performing players in a particular industry improve their performance such that the rest of the contenders remain competitive. This study investigates the concept of constrained improvement from a frontier analysis perspective, develops a systematic implementation framework, and explores a novel application of sensitivity analysis in Data Envelopment Analysis (DEA). Original programming approaches are developed to discover the stability region considering a variable returns to scale. The objective is to determine the extent to which the input and output of a decision-making unit (DMU) can be improved or worsened before the configuration of the efficient frontier changes. Furthermore, the permissible change radius for the decision-making unit is identified, considering all possible change directions. The applicability of the approach is demonstrated using numerical examples.

A Hybrid Algorithm of LSTM and Factor Graph for Improving Combined GNSS/INS Positioning Accuracy during GNSS Interruptions.

In urban road environments, global navigation satellite system (GNSS) signals may be interrupted due to occlusion by buildings and obstacles, resulting in reduced accuracy and discontinuity of combined GNSS/inertial navigation system (INS) positioning. Improving the accuracy and robustness of combined GNSS/INS positioning systems for land vehicles in the presence of GNSS interruptions is a challenging task. The main objective of this paper is to develop a method for predicting GNSS information during GNSS outages based on a long short-term memory (LSTM) neural network to assist in factor graph-based combined GNSS/INS localization, which can provide a reliable combined localization solution during GNSS signal outages. In an environment with good GNSS signals, a factor graph fusion algorithm is used for data fusion of the combined positioning system, and an LSTM neural network prediction model is trained, and model parameters are determined using the INS velocity, inertial measurement unit (IMU) output, and GNSS position incremental data. In an environment with interrupted GNSS signals, the LSTM model is used to predict the GNSS positional increments and generate the pseudo-GNSS information and the solved results of INS for combined localization. In order to verify the performance and effectiveness of the proposed method, we conducted real-world road test experiments on land vehicles installed with GNSS receivers and inertial sensors. The experimental results show that, compared with the traditional combined GNSS/INS factor graph localization method, the proposed method can provide more accurate and robust localization results even in environments with frequent GNSS signal loss.

A two‐stage reactive power optimization method for distribution networks based on a hybrid model and data‐driven approach

AbstractThe uncertainty of distributed energy resources (DERs) and loads in distribution networks poses challenges for reactive power optimization and control timeliness. The computational limitations of the traditional algorithms and the development of artificial intelligence (AI) based technologies have promoted the advancement of hybrid model‐data‐driven algorithms. This article proposes a two‐stage reactive power optimization method for distributed networks (DNs) based on a hybrid model‐data‐driven approach. In the first stage, based on the topology and line parameters of the DN, as well as forecasts of loads and renewable energy outputs, a mixed‐integer second‐order cone programming (MISOCP) algorithm is used to control the on‐load tap changer (OLTC) positions on an hourly day‐ahead basis. In the second stage, leveraging deep learning technology, the real‐time reactive power output of photovoltaics (PV) and wind power units is controlled at a 5‐min time scale throughout the day. Specifically, using traditional solvers, the global optimal reactive power output for PV and wind power units is determined first, corresponding to various load and renewable energy output scenarios. Then, neural networks are trained to map node power to the optimal reactive power outputs of renewable energy units, capturing the complex physical relationships. For the second stage, a transformer network framework with a self‐attention mechanism and multi‐head attention for deep learning training is applied to uncover the intrinsic and physical spatial relationships among high‐dimensional features. The proposed method is tested on a modified IEEE 33‐bus system with multiple distributed renewable energy sources. The case study results demonstrate that the proposed hybrid model‐data‐driven algorithm effectively coordinates day‐ahead and real‐time controls of various devices, achieving real‐time model‐free optimization throughout the day. Compared to traditional deep neural networks (DNNs) and convolutional neural networks (CNNs), the transformer network provides superior reactive power optimization results.

Flexible resource allocation optimization model considering global K-means load clustering and renewable-energy consumption

Abstract Vigorously developing flexible resources in power systems will be the key to building a new power system and realizing energy transformation. The investment construction cost and operation cost of various flexible resources are different, and the adjustment ability is different in different timescales. Therefore, the optimization of complementary allocation of various resources needs to take into account the economy and adjustment ability of different resources. In this paper, the global K-means load clustering model is proposed and the 365-day net load is reduced to eight typical daily net loads by clustering. Secondly, a two-level optimization model of flexible resource complementary allocation considering wind power and photovoltaic consumption is constructed. The flexible resources involved include the flexible transformation of thermal power, hydropower, pumped storage, energy storage, and demand response. The upper-layer model optimizes the capacity allocation of various flexible resources with the minimum investment and construction cost as the goal and the lower layer optimizes the operating output of various units with the minimum operating cost as the goal. The results of the example analysis show that the flexible capacity of thermal power units has nothing to do with the abandonment rate of renewable energy. As the abandonment rate of renewable energy decreases, the optimal capacity of pumped storage, electrochemical energy storage, and hydropower units increases. When the power-abandonment rate of renewable energy is 5%, the optimal allocation capacity of thermal power flexibility transformation, pumped storage, electrochemical energy storage, hydropower unit, and adjustable load in Province A is 5313, 17 090, 5830, 72 113, and 4250 MW, respectively. Under the condition that the renewable-energy abandonment rate is 0, 5%, and 10% respectively, the configured capacity of pumped storage is 20 000, 17 090, and 14 847 MW, respectively.

Low-carbon operation strategy of virtual power plant considering progressive demand response

Adapting virtual power plants (VPPs) to the development of new power systems has gradually become a trend. Meanwhile, the economical and effective use of demand-side resources in VPPs has become a research hotspot in the field of new energy. This paper proposes a low-carbon operation strategy with progressive demand response (DR). A DR model id established by evaluating the scheduling potential of low-carbon and flexible loads and the DR transfer of different users motivated by incentive mechanisms. Because the changing carbon price in carbon trading also affects the response transfer amount, it is beneficial to reduce the operating cost of the energy system by using consumers’ surplus value according to consumer psychology to guide energy users to participate in DR. Finally, wind, light, user load data and VPP internal unit output are optimized in rolling mode over multiple time scales, and the optimal adjustment results of the previous day are taken as the basis. At the same time, the output deviation penalty function is added to further reduce the system cost. The nonlinear model of DR cost is transformed into a mixed integer linear model, which is modeled on MATLAB software and solved by a CPLEX optimization solver. The case analysis shows that the progressive DR strategy may increase the total operating cost of the system, but reduce the cost of the carbon emission. The proposed model and method can not only improve the feasibility and effectiveness of market supervision strategy, but also provides a useful reference for operators and demand-side users in energy systems to choose strategies.

Configuration synthesis and screening method for multiple closed-loop unit tandem mechanisms

To broaden the methods of configuration synthesis, this study proposes a new method for configuration synthesis and screening of multiple closed-loop unit tandem mechanisms. Firstly, some closed-loop configurations are taken as basic units, and the closed-loop basic units are synthesized in series under the condition of considering the basic unit types, contacts, and input and output relationships. Secondly, the connectivity of the mechanism is obtained by weighting the adjacency matrix, and the entropy weight method is used for the first time to establish a comprehensive evaluation index coefficient to assess the quality of the mechanism. Then, an improved simulated annealing-greedy algorithm is proposed and an algorithm software screening platform is built to screen mechanisms. Finally, the multi-fingered grasper is used as design case to verify the practicality of the proposed configuration synthesis and screening method. This study can provide a reference for theoretical research on the configuration synthesis and screening methods of multiple closed-loop unit tandem mechanisms.

How increased heifer growth rate and reduced dairy cow replacement rate can improve farm economy and reduce greenhouse gas emissions - a win to win situation?

Rearing of replacement heifers makes up a significant part of the total costs in dairy farming. Nevertheless, the average age at first calving for dairy heifers still stays well above 2 years in many countries. This study examined the economic and environmental impacts of increased heifer growth rates and reduced replacement rates on Norwegian dairy farms. The current average growth rate in Norway (baseline scenario) was compared to an accelerated growth rate scenario. Within each of the two growth rate scenarios, we compared three different cow replacement rates. A farm account survey dataset containing physical and economic data on 311 Norwegian farms was clustered into three farm groups: small, medium, and large. To model economic consequences, we used the whole-farm linear programming model ScotFarm. A life cycle analysis model was used to model the environmental impacts of the baseline scenario and an accelerated growth rate scenario on the three farm groups. Accelerated heifer growth rate had a positive effect (14–28%) on farm annual gross margin depending on farm size. While accelerated growth rate resulted in only minor reductions in total emissions at farm level compared to the baseline scenario, reduced replacement rate lowered total farm level emissions by up to 8%, and emissions per unit of output by up to 6%. We conclude that an accelerated heifer growth rate scenario could potentially increase farm gross margin by some 14–28% compared with a baseline growth rate scenario. Reducing the replacement rate would be more efficient to reduce farm−level greenhouse gas emissions.

Unlocking Excellence: Khageri Irrigation System Benchmarking in Chitwan

The objective of the study was to assess the present service delivery, financial and productivity indicators. The performance of five branch canals i.e. Branch Canal 1, Branch Canal 4, Branch Canal 6, Branch Canal 7 and Branch Canal 8 of the Khageri Irrigation System was evaluated for 2023 AD using fifteen performance indicators under water supply, financial performance and agricultural productivity categories suggested by the International Programme for Technology and Research in Irrigation and Drainage (IPTRID). 331 farmers were interviewed in respective five branch canals from head, middle, and tail portions of the command area in order to get a representative sample. The data obtained from the interview was analysed and interpreted to calculate the values of the performance indicators. It was also found that the canal capacity and scheme infrastructure was not the limiting cause of this observation according to the calculation of water delivery capacity (1.08). The irrigation service fee charged was not sufficient to pay for the Operation and Maintenance (O&M) cost according to the calculation of revenue collection performance (0.5, 50%). Land productivity in this system was found to be unsatisfactory according to the calculations of output per unit command area (13084.76 Rs/ha), ouput per unit irrigated area (13216.93 Rs/ha) and output per unit irrigation supply (1.82 Rs/m3).