Dynamic MPPT Controller Using Cascade Neural Network for a Wind Power Conversion System with Energy Management

With the growing demand for renewable energy, solar photovoltaic (PV) systems have gained popularity as a reliable source of clean electricity. However, the performance of these systems can be limited by factors such as suboptimal maximum power point tracking (MPPT) algorithms. In order to improve the power generation efficiency of PV systems, it is important to evaluate the performance of dynamic MPPT algorithms that can adapt to varying operating conditions. Traditionally, such evaluations have been time consuming and expensive, often requiring extensive testing and measurement equipment. In this paper, we propose a novel approach to evaluate dynamic MPPT performance very quickly and simply using PSIM software. This approach enables accurate and efficient evaluation of MPPT performance under a wide range of operating conditions, while minimizing the cost and time involved in traditional testing methods. When applying the proposed method to a 3.7 kW inverter using the traditional perturbation and observation (P and O) method, we found that the highest average efficiency was 98.92% at an MPPT control period of 0.1s and a voltage perturbation of 1 V. This evaluation technique provides valuable insights into the design and optimization of more efficient MPPT control algorithms, leading to improved power generation efficiency and increased adoption of solar PV systems.

SummaryDue to the stochastic and unpredictable nature of wind speed, extraction of maximum power from the wind turbine system becomes an attractive control objective. Therefore, to capture more wind power, various conventional maximum power point tracking (MPPT) schemes are developed for standalone small‐scale variable speed wind power generation system (VSWPGS). This manuscript proposes novel adaptive step size (ASS) and drift‐free ASS (DF‐ASS) MPPT methods for a permanent magnet synchronous generator (PMSG)‐based VSWPGS. The control schemes achieve maximum power point (MPP) without using mechanical sensors like speed encoder and anemometer. The proposed MPPT controllers are implemented on a laboratory‐scale DC motor‐PMSG‐based wind power generation system (WPGS) using OPAL‐RT real‐time digital platform. The ASS and DF‐ASS methods capture more power, reduce the steady‐state power oscillation around MPP, and improve the tracking speed. Also, the proposed DF‐ASS control scheme avoids drift phenomenon during wind speed increase condition. The comparative experimental results of proposed ASS and DF‐ASS methods with respect to the fixed step size MPPT schemes show better dynamic and steady‐state performance, making it pertinent for small‐scale WPGS.

This study presents a maximum power point tracking (MPPT) method and controller for a multi-channel ( N -channel) photovoltaic solar system. The N -channel single-sensor MPPT (SS-MPPT) controller is able to track the maximum power point (MPP) of each solar panel using only one output current sensor in the N -channels. Compared with a conventional N -channel MPPT system which requires N voltage sensors, N current sensors, 2 N analogue-to-digital converters (ADCs) (or an ADC with 2 N channels) and N -MPPT controllers along with the associated conditioning circuitries, the proposed method only requires one sensor, one ADC and one MPPT controller. In the proposed N -channel SS-MPPT controller, the MPPT for each channel is achieved by tracking the maxima of the output (load) current (resulting in the maxima of the system output power) using the Perturb and Observe algorithm and the maxima of each channel power via channel output current difference optimisation. Two algorithms which can be used to realise the SS-MPPT controller are discussed and compared. The concept and operation of the SS-MPPT controller is presented, analysed and verified by results obtained from a proof-of-concept experimental prototype.

When the battery or supercapacitor is connected to the output of a PV system, the conventional voltage equation expressing its mathematical model usually must be replaced by the current relationship to study the maximum power point tracking (MPPT) control theory. However, hitherto, there is a lack of an attempt to disclose the current constraint relationships at the maximum power point (MPP), which leads to the potential risk of MPPT failure. To solve this problem, in this paper, the MPPT constraint conditions on the basis of currents are built and then a new MPPT control strategy is proposed. In this strategy, a linearized model parameter of a PV cell is used as the bridge to find the current relationships. On the basis of them, some expressions involving the duty cycle are built to directly calculate the control signal of the MPPT controller. Meanwhile, an implementation method is designed to match this proposed MPPT strategy. Finally, some simulation experiments are conducted. The simulation results verify that the proposed MPPT constraint expressions are accurate and workable and that the proposed MPPT strategy and its implementation process are feasible and available. In addition, the simulation results also show that the proposed MPPT strategy has a better MPPT speed and the same MPPT accuracy when the P&O method and fuzzy algorithm are compared. By this work, the MPPT constraint conditions based on current relationships are first found, representing a breakthrough in disclosing the inherent relationships between different currents when the PV system is operating around the MPP.

In this paper, a control scheme for wind energy conversion systems (WECS) with induction generator is presented that is composed of a copper loss minimization (CLM) controller and a maximum power point tracking (MPPT) controller. The CLM is accomplished by controlling the excitation current of the induction generator and the MPPT control is attained by controlling the rotational speed of the wind turbine. The suggested control scheme can be easily implemented because, neither the measurement of the wind speed nor the knowledge of the wind turbine characteristics are required. Moreover, it exhibits fast dynamic response and thus, it can follow the fast dynamics of the wind. The generator is connected to the power grid by means of two vector controlled back-to-back converters. Experimental results are presented to validate the cooperation of the CLM controller with the MPPT controller.

The MPPT (Maximum Power Point Tracking) control topology based on Fuzzy logic, and analysis of the Vienna Rectifier for the small-scale Wind Turbine System (WTS) is proposed in this paper. The PMSG (Permanent Magnet Synchronous Generator) of the WTS generates the output power that is fluctuated due to irregular wind velocity, which has to be controlled for the smooth output power. Many controlstrategies are projected by the researchers for the settled power output, but the conventional control techniques getting more complex. One of the simple and robust methods that enable for the MPPT is fuzzy logic control. The above mechanism regulates the speed of PMSG and the DC power output. Moreover, it is engaged in the parameter optimization and the speed control of the PMSG. A fuzzy logic MPPT controller based Vienna Rectifier is used in this paper for a 1kW WTS with improved efficiency and reduced harmonics, and the results are justified using MATLAB/Simulink.

A maximum power point tracking (MPPT) controller was used to make the photovoltaic (PV) module operate at its maximum power point (MPP) under changing temperature and sunlight irradiance. Under partially shaded conditions, the characteristic power–voltage (P–V) curve of the PV modules will have more than one maximum power point, at least one local maximum power point and a global maximum power point. Conventional MPPT controllers may control the PV module array at the local maximum power point rather than the global maximum power point. MPPT control can be also implemented by using soft computing methods (SCM), which can handle the partial shade problem. However, to improve the robustness and speed of the MPPT controller, a hybrid MPPT controller has been proposed that combines two SCMs, the Genetic Algorithm (GA) and Ant Colony Optimization (ACO). Matlab was used in a simulation of a GA-ACO MPPT controller where four SunPower SPR-305NE-WHT-D PV modules with a maximum power of 305.226 W connected in series were used under conditions of partial shade to investigate the performance of the proposed MPPT controller. The results obtained were analyzed and compared with others obtained under perturb and observe (P&O) MPPT and conventional ACO MPPT controllers were observed.

Compared with typical current controlled PV inverters, voltage controlled PV inverters based on droop control are easier to achieve seamless transfer between the stand-alone mode and the grid-connected mode. This paper achieves both seamless transfer and the maximum power point (MPP) tracking (MPPT) by adjusting droop curves dynamically, combining MPPT and P-f droop control. The DC voltage is difficult to control during grid connecting which will affect both the stability of the system and the MPPT efficiency. To solve this problem, a control method is proposed which includes an active power inner loop and a DC voltage outer loop. At last simulation and experimental results validate the proposed control strategy and show that DC voltage response is fast and stable. Both seamless transfer and MPPT operation modes are realized.

An efficient maximum power point tracking (MPPT) controller is a crucial part of solar photovoltaic (PV) system, which can handle the non-linear characteristics of a solar PV array. In this study, a proposed solar PV system with boost converter and dc load is modeled and compared according to the operating characteristics (i.e. voltage, current, power and efficiency under varying solar irradiance and cell temperature) using five conventional and modern MPPT techniques. The proposed solar PV system and MPPT controllers are modeled and simulated in MATLAB Simulink platform. Among these five MPPT controllers, artificial neural network (ANN) MPPT controller provides the highest efficiency of 97.55% and produces less voltage and power fluctuations. The novelty of this paper is that it focuses on the key characteristics and simulated results of the five MPPT techniques to make a comparison between them.

In order to maximize the power generation capacity of solar cells, the power rate transmission mode of power controller generally adopts the maximum power point tracking (MPPT) control mode. Among them, the energy system with centralized topology only contains one MPPT controller, which cannot ensure that each photovoltaic module works at its maximum power point, resulting in a significant decline in the conversion efficiency of the energy system. The architecture of distributed energy system includes parallel and series. Compared with the parallel MPPT controller, the voltage ratio of the series MPPT controller can be smaller, which is helpful to improve the conversion efficiency of the controller. Firstly, a distributed energy system based on series MPPT is proposed, and a start-up strategy without communication is proposed. Then, according to the characteristics of the proposed system, a high-precision and fast MPPT control strategy is proposed. Then, according to the system architecture and control strategy, the parameters of MPPT controller are designed. Finally, an experimental platform is built to verify the effectiveness of the proposed energy system architecture and control strategy.

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Among several types of fuel cells available in the market, proton exchange membrane fuel cell (PEMFC) is characterized by low operating temperature, high efficiency and long lifetime. These advantages have prompted the PEM fuel cell to enter into several applications such as vehicle power sources, portable power and backup power applications. However, the PEM fuel cell faces several challenges due to the dependency of the output power on the operating condition like cell temperature and membrane water content. Under changing operating conditions, there is only one unique operating point for the fuel cell system with maximum output. Therefore, for better operation and optimal exploitation, the extraction of maximum power from PEM fuel cell is indispensable. This paper deals with the development golden section search (GSS)-based maximum power point tracking (MPPT) controller for PEMFC power system. To our knowledge, this paper is a first, if modest, attempt to develop a fuel cell MPPT controller based on golden section algorithm. The proposed GSS-based MPPT has been implemented and validated on fuel cell power system composed of 7 kW PEMFC supplying a resistive load via a DC/DC boost converter controlled using the proposed MPPT. Simulation results obtained using MATLAB/Simulink show that the proposed GSS MPPT outperforms the variable step size incremental conductance one in all test cases including cell temperature and membrane water content variations in terms of static as well as dynamic performances regarding all used metrics reducing by the way the response time by 34.33%, the overshoot and ripple by around 100% and with neglect oscillation around MPP improving as a consequence the fuel cell efficiency.

This paper addresses the design and analysis of the control system for a Wind Energy Conversion System (WECS) with a Permanent Magnet Synchronous Generator (PMSG) and its application for isolated green hydrogen production. The designed control maximizes the wind turbine (WT) power generation by regulating the electrolyzer current consumption, where the electrolyzer operates as a controlled load, ensuring power balance in the system and enabling the generation of maximum power from the WECS without the use of any energy storage system. The system involves directly integrating a WECS with an alkaline electrolyzer (AEL), and its configuration includes a WT with a PMSG, an AC/DC voltage source converter (VSC) acting as the generator side converter (GSC), and a DC/DC converter acting as the electrolyzer side converter (ESC) with Maximum Power Point Tracking (MPPT) control for the WT. The proposed control system has been fully modeled and tested through simulations in Matlab/Simulink, demonstrating its reliable performance under variable wind speeds. Simulation results illustrate how, over rated wind speeds, the pitch control limits the rotational speed and power to their maximum values, and at under rated wind speeds, the ESC control regulates the AEL current following the MPPT of the WT, while the GSC control maintains the DC bus voltage at its designated nominal value. Overall, the proposed control system shows robust and effective performance across the entire range of possible operational points, ensuring no wind generation power losses by following the maximum power point.

This paper presents a new wind speed sensorless maximum power point tracking (MPPT) controller for variable speed wind energy conversion systems (WECS). The MPPT control of the WECS, in this work, is achieved using optimum speed - power curve of the WECS after taking into account the changes in system efficiency while operating at different operating points due to the change in wind speed. The method proposed is based on the fact that the optimum output power of a WECS at a certain wind speed depends upon the total mechanical power developed by the turbine and efficiency of the WECS at the corresponding optimum speed (OS) of rotation of the turbine. Implementation of this control algorithm requires the knowledge of output power and air density as its inputs in addition to requiring the knowledge of turbine parameters. The proposed method is analyzed experimentally in a variable speed direct drive permanent magnet synchronous generator (PMSG) WECS using a wind turbine emulator.

In wind energy conversion systems, maximum power point tracking (MPPT) performance is crucial, as it is directly related to wind speed variability and the characteristics of the equipment used. Maximum power point tracking controllers are essential for optimizing the efficiency of wind power generation. This paper presents the development of three distinct approaches to maximum power point tracking: the classical perturb and observe (P&O) method, and two other techniques based on artificial intelligence, namely long short-term memory (LSTM) networks and deep neural networks (DNNs). Rather than focusing solely on the development of an intelligent neural network-based maximum power point tracking model, our work emphasizes the design of a deep neural network controller with an optimized architecture and a reduced number of layers and neurons per layer, thereby simplifying its implementation in embedded process control units while maintaining high maximum power point tracking performance. The results obtained show that our optimized deep neural network model identifies the point of maximum power more effectively than other techniques, demonstrating remarkable performance in terms of response time, accuracy, and the quality of the generated power.