MPPT-Based Optimal NNC-hMOGA/BPA for Photovoltaic Water Pumping System

Background: Despite so many developments, most of the farmers in the rural areas are still dependent on rainwater, rivers or water wells, for irrigation, drinking water etc. The main reason behind such dependency is non-connectivity with the National grid and thus unavailability of electricity. To extract the maximum power from solar photovoltaic (SPV) based system, implementation of Maximum Power Point Tracking (MPPT) is mandatory. PV power is intermittent in nature. Variation in the irradiation level due to partial shading or mismatching phenomena leads to the development of modular DC-DC converters. Methods: A stand-alone Multi-Input Dual-Output (MIDO) DC-DC converter based SPV system, is installed at a farm; surrounded with plants for water pumping with stable flow (not pulsating) along with battery energy storage (BES) for lighting. The proposed work has two main objectives; first to maximize the available PV power under shadowing and mismatching condition in case of series/ parallel connected PV modules and second is to improve the utilization of available PV energy with dual loads connected to it. Implementation of proposed MIDO converter along with BES addresses these objectives. First, MIDO controller ensures the MPPT operation of the SPV system to extract maximum power even under partial shading condition and second, controls the power supplied to the motor-pump system and BES. The proposed system is simulated in MATLAB/ SIMULINK environment. Real-time experimental readings under natural sun irradiance through hardware set-up are also taken under dynamic field conditions to validate the performance. Results and Conclusion: The inherent advantage of individual MPPT of each PV source in MIDO configuration, under varying shadow patterns due to surrounding plants and trees is added to common DC bus and therefore provides a better impact on PV power extraction as compared to conventional PV based water pumping system. Multi-outputs at different supply voltages is another flag of MIDO system. Both these aspects are implemented and working successfully at 92.75% efficiency.

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Performance investigation on ANFIS and FFNN assisted direct and indirect PV-fed switched reluctance motor water pumping system

International Journal of Modelling and Simulation ◽

10.1080/02286203.2021.1875288 ◽

2021 ◽

pp. 1-13

Author(s):

Vijay Babu Koreboina ◽

Narasimharaju B L ◽

Vinod Kumar D M

Keyword(s):

Switched Reluctance Motor ◽

Switched Reluctance ◽

Pumping System ◽

Water Pumping ◽

Reluctance Motor

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Indirect Feedback Measurement of Flow in a Water Pumping Network Employing Artificial Intelligence

Sensors ◽

10.3390/s21010075 ◽

2020 ◽

Vol 21 (1) ◽

pp. 75

Author(s):

Thommas Kevin Sales Flores ◽

Juan Moises Mauricio Villanueva ◽

Heber P. Gomes ◽

Sebastian Y. C. Catunda

Keyword(s):

Artificial Intelligence ◽

Nonlinear Models ◽

Control Strategies ◽

Experimental Tests ◽

Feedback System ◽

Indirect Measurement ◽

High Price ◽

Maximum Relative Error ◽

Pumping System ◽

Water Pumping

Indirect measurement can be used as an alternative to obtain a desired quantity, whose physical positioning or use of a direct sensor in the plant is expensive or not possible. This procedure can been improved by means of feedback control strategies of a secondary variable, which can be measured and controlled. Its main advantage is a new form of dynamic response, with improvements in the response time of the measurement of the quantity of interest. In water pumping networks, this methodology can be employed for measuring the flow indirectly, which can be advantageous due to the high price of flow sensors and the operational complexity to install them in pipelines. In this work, we present the use of artificial intelligence techniques in the implementation of the feedback system for indirect flow measurement. Among the contributions of this new technique is the design of the pressure controller using the Fuzzy logic theory, which rules out the need for knowing the plant model, as well as the use of an artificial neural network for the construction of nonlinear models with the purpose of indirectly estimating the flow. The validation of the proposed approach was carried out through experimental tests in a water pumping system, fully automated and installed at the Laboratory of Hydraulic and Energy Efficiency in Sanitation at the Federal University of Paraiba (LENHS/UFPB). The results were compared with an electromagnetic flow sensor present in the system, obtaining a maximum relative error of 10%.

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