Maximum Power Point Tracking Mode Research Articles

Autonomous Street Lighting System based on Solar Energy and LEDs

This paper introduces a solar-powered autonomous street lighting system designed primarily for areas without access to the grid, such as rural roads and intersections. It utilizes solar panels as the primary energy source, with batteries serving as backup, and employs light-emitting diodes (LEDs) for illumination. The proposed system is optimized for efficiency by employing direct current (DC) power at all stages. To replace a 70W high-pressure sodium (HPS) lamp, an LED fixture design is discussed, considering human scotopic vision. Experimental outcomes for the LED driver and battery charger are provided, demonstrating features such as maximum power point tracking (MPPT), constant current, and constant voltage modes. The system ensures MPPT under varying solar irradiance and temperature conditions by analyzing the battery charger input impedance.

Extreme learning machine based on BDE feature selection to detect fault scenarios in grid-connected PV systems under MPPT mode

This paper considers real-time data-driven adaptive fault detection (FD) in grid-connected PV (GPV) systems under maximum power point tracking (MPPT) modes during large variations. Faults under MPPT modes remain undetected for longer periods, introducing new protection challenges and threats to the system. An intelligent FD algorithm is developed through real-time multi-sensor measurements and virtual Micro Phasor Measurement Unit (Micro-PMU) estimations. The high-dimensional and high-frequency multivariate features vary over time, and computational efficiency becomes crucial to realizing online adaptive FD. The goal of this study is to present an artificial intelligence (AI) technique for detecting seven faults: inverter fault, feedback sensor fault, grid anomaly, nonhomogeneous partial shading, open circuit in PV array, MPPT controller fault, and boost converter controller fault. In this work, it was found that the application of Extreme Learning Machine (ELM) plays an important role in fault detection and localization. Nine (9) statistical features and eight (8) wavelet packet parameters are extracted from the data based on multiple default values. These features were used as an input vector to train and test the ELM and determine whether the system is operating under normal conditions or is faulty. The BDE feature selection algorithm is adopted to optimize the seven-fault classification procedure to reduce the number of features. The results showed that the Extreme Learning Machine (ELM), based on statistical parameters followed by BDE, can detect faults with high accuracy (98.3%) compared to a case without optimization.

Automatic fault detection in grid-connected photovoltaic systems via variational autoencoder-based monitoring

Anomaly detection is indispensable for ensuring the reliable operation of grid-connected photovoltaic (PV) systems. This study introduces a semi-supervised deep learning approach for fault detection in such systems. The method leverages a variational autoencoder (VAE) to extract features and identify anomalies. By training the VAE on normal operation data, a compact latent space representation is created. Abnormal observations, indicating faults, exhibit distinct feature vectors in this latent space. Multiple anomaly detection algorithms, including Isolation Forest, Epileptic Envelope, Local Outlier Factor, and One-Class SVM, are employed to discern normal and abnormal observations. This semi-supervised approach only requires fault-free data for training, without labeled faults, making it attractive in practice. A publicly available dataset, the Grid-connected PV System Faults (GPVS-Faults) dataset, which includes data from a PV plant operating in both maximum power point tracking (MPPT) and intermediate power point tracking (IPPT) switching modes, is used for evaluation. The proposed approach is assessed across various fault scenarios, such as partial shading, inverter faults, and MPPT/IPPT controller faults in boost converters. The outcomes underscore the effectiveness of VAE-based techniques in accurately identifying these faults, with accuracy rates reaching up to 92.90% for MPPT mode and 92.99% for IPPT mode, thus contributing to the robustness of fault detection in grid-connected PV systems.

Robust power control for PV and battery systems: integrating sliding mode MPPT with dual buck converters

This paper presents a comprehensive exploration of an integrated Buck-Boost converter and Sliding Mode Control (SMC) Maximum Power Point Tracking (MPPT) system for optimizing photovoltaic energy conversion. The study focuses on enhancing solar energy extraction efficiency, regulating output currents, and ensuring effective battery utilization. Through a systematic analysis of converter component sizing and operational modes, the paper delves into the intricacies of the Buck-Boost converter. The unique contribution lies in the innovative integration of SMC with the traditional Perturb and Observe (P&O) algorithm, providing robust and adaptive MPPT under varying environmental conditions. Additionally, the paper introduces a battery management system with three distinct modes, namely, Charging, Direct, and Discharging, offering intelligent control over critical scenarios. Simulation results underscore the robustness of the proposed system under diverse conditions, demonstrating its effectiveness in managing power distribution based on battery charge levels, even in scenarios of insufficient solar power. Overall, this research significantly contributes to advancing the understanding of PV/battery systems and offers a practical, sustainable solution for optimizing energy production, distribution, and storage, marking a substantial stride towards a more efficient and sustainable energy future.

Adaptive integral sliding mode MPPT control for wind turbines with fixed‐time convergence

AbstractIn the context of increasing renewable energy penetration and the imperative for efficient utilization, this paper explores the application of maximum power point tracking (MPPT) control for wind turbines. The objective is to optimize wind power generation by maximizing energy capture efficiency while addressing challenges associated with behind‐the‐meter renewables, load flexibility, and encouraging prosumers' participation. This paper proposes an adaptive integral sliding mode MPPT control strategy for wind turbines in the presence of external disturbance and model uncertainties. A new fixed‐time integral sliding mode manifold is constructed, such that the singularity problem can be avoided in the controller design without using any piecewise continuous functions. Then, an adaptive fixed‐time MPPT controller is developed to ensure that the tracking error can converge to a small neighbourhood around zero in fixed time, where the upper bound of the convergence time can be pre‐set based on actual engineering needs by selecting appropriate parameters. By designing update law, no prior knowledge on the upper bound of the lumped uncertainties is required anymore in advance. Stability analysis of the closed‐loop system is conducted using Lyapunov stability theory, and the effectiveness of the proposed strategy is validated through numerical simulations.

Grid Forming Fast Frequency Response for PMSG-Based Wind Turbines

Electric power systems are undergoing a rapid transition from fuel-based generation using synchronous generators to renewable generation interfaced by power electronics. In this context, a key challenge is using renewable generation with limited controllability to contribute to power system stability. In this work, we investigate grid-forming control of permanent magnet synchronous generator (PMSG) wind turbines. The proposed control and curtailment strategy supports the entire spectrum of standard functions of grid-following (e.g. maximum power point tracking (MPPT) and grid-forming control (e.g., primary frequency control) without explicit mode switching. A detailed case study is used to compare the performance of the proposed control at operating points corresponding to MPPT and frequency control with standard grid-forming and grid-following controls. The results demonstrate that (i) the proposed energy balancing grid forming control is self-synchronizing in MPPT mode, (ii) the (limited) energy storage and controllability of wind turbines can be adequately utilized to provide grid support, and (ii) the proposed control exhibits good performance under variable wind speeds and does not result in a significant increase of wind turbine fatigue loads.

A novel dynamic inertial control of wind turbines based on event size estimation

High penetration of variable renewable energy sources causes volatile grid frequency movements in low inertia systems. Large-scale wind turbines (WTs) are stipulated to participate in frequency control upon occurring a disturbance event. This paper proposes a novel inertial control scheme aimed at improving the frequency nadir based on frequency event size (FES) estimation. Upon occurring a frequency event, the WTs immediately inject a stepwise active power into the grid, and its corresponding frequency deviation is measured to estimate the FES using mean absolute error solution of a sinusoidal exponential function regression. Subsequently, an inertial control is developed to mitigate the frequency movements taking into account the available kinetic energy in rotating masses of WTs and estimated FES. To ensure a safe rotor speed recovery to its maximum power point tracking mode within this scheme, the rotor speed recovery time, and the second frequency drop can be flexibly adjusted. The WTs overproduction and recovery stages are dynamically adapted to wind speed fluctuations based on the defined modifications and rules. Additionally, the scheme considers the latency of communication delay during FES estimation. The proposed method is validated using a large-scale wind farm integrated into IEEE 9-bus and 39-bus test systems in Digsilent PowerFactory. Comparison results with existing methods in the literature demonstrate that the proposed scheme achieves better frequency nadir improvement across various types and sizes of events and wind speeds. Furthermore, it exhibits robust performance against communication latencies. Finally, the experimental results support the findings in terms of FES estimation.

Study on impact of photovoltaic power tracking modes on photovoltaic-photothermal performance of PV-PCM-Trombe wall system

Due to the lack of research on the impact of photovoltaic (PV) power tracking methods on the performance of Building-Integrated Photovoltaic-Thermal (BIPV/T) systems, this study comparatively analyzes the photovoltaic-photothermal performance of the PV-PCM-Trombe Wall system operating in Pulse Width Modulation (PWM) and Maximum Power Point Tracking (MPPT) modes during winter passive heating conditions. Additionally, the study investigates the influence of these modes on the thermal characteristics of buildings. The results indicate that the MPPT strategy achieved a PV efficiency of 15.50 %, while the PWM strategy resulted in a significant reduction to 12.72 % (a decrease of 2.78 % compared to MPPT). Importantly, both strategies effectively utilized solar energy for passive space heating without noticeable differences in photothermal performance. Furthermore, the study observed that the upper part of the PCM plate reached higher peak temperatures earlier in the vertical dimension. Experiment A exhibited peak temperatures ranging from 33.57 °C to 34.76 °C, while Experiment B had a wider range of 27.99 °C to 36.10 °C. Minimal temperature variations were observed across the PCM plate in the thickness direction, measuring only 0.60 °C in Experiment A and 0.70 °C in Experiment B. These research findings provide valuable insights into the roles and impacts of different PV power tracking strategies in PV-PCM-Trombe wall systems.

Control design and multimode power management of WECS connected to HVDC transmission line through a Vienna rectifier

This paper deals with nonlinear control and optimal management of wind energy conversion systems connected to HVDC transmission lines. Specifically, the energy system under study consists of a wind turbine driving a permanent magnet synchronous generator, the latter supplies energy to a power grid through a Vienna rectifier and a DC link. To compensate for intermittent nature of wind power and load demand variations, we design a voltage controller as well as a novel energy management strategy that accounts for the available wind power and DC load demands. In the considered management, distinction is made between two operation modes: i) the maximum power point tracking mode using an intelligent adaptive optimizer, where only electrical parameter measurements are used to ensure maximum power extraction; ii) the adaptive power point tracking mode employing demand-driven production. In both modes, the power factor requirement is ensured, regardless the weather conditions and load demand variations, enhancing thus the aerogenerator’s efficiency. One more novelty of the proposed control and management system is the online estimation of the DC voltage, avoiding thus the use of a physical sensor. The performances of the control and management system are highlighted by several simulations, showing the achievement all control objectives.

Energy Conversion Optimization Method in Nano-Grids Using Variable Supply Voltage Adjustment Strategy Based on a Novel Inverse Maximum Power Point Tracking Technique (iMPPT)

This paper introduces a novel power supply voltage adjustment strategy that can determine the optimum voltage value based on the amount of absorbed power. The novel automatic voltage adjustment technique was called inverse maximum power point tracking (iMPPT). The proposed control strategy consists of a modified maximum power point tracking (MPPT) algorithm (more precisely the P&O method). In this case, the modified MPPT technique establishes the minimum value of the input absorbed power of a consumer load served by a switched-mode power supply (SMPS). The iMPPT adjusts the input power by modifying the input voltage of the main power supply. The served loads are connected to the variable power supply via an interfacing power electronics converter that performs the automatic voltage regulation function (AVR). The optimal value of the input voltage level can be achieved when the input power of the automatic voltage regulation converter is at a minimum. In that case, the energy conversion efficiency ratio is at a maximum, and the overall losses related to the front-end power stage are at a minimum. The proposed technique can also be considered a Maximum Efficiency Tracking (MET) method. By performing the inverse operation of a maximum power point tracking algorithm on the input demanded power of a switched mode power supply (SMPS), the optimum input voltage level can be determined when the maximum energy conversion ratio (related to a given load level) is achieved. The novel proposed iMPPT method can improve the energy conversion ratio from 85% up to approximately 10% in the case of an output power level of 800 W served by a synchronous buck converter at the input voltage level of 350 V. The total amount of recovered power in this situation can be approximately 100 W.

An Improved FCS-MPC Strategy for Low-Frequency Oscillation Stabilization of PV-Based Microgrids

With the rapid promotion of photovoltaic (PV) power generation, the local consumption of distributed PV power generation at medium and low voltage levels widely forms PV-based microgrids. The continuous increase in the penetration rate of uncertain PV power generation requires it to actively participate in the frequency and voltage regulation of weak microgrid systems. Finite control set-model predictive control (FCS-MPC) for voltage source converter (VSC) shows remarkable advantages in fast dynamic response and robustness compared with traditional cascaded linear control methods. However, the traditional FCS-MPC method for VSC without considering the DC-link voltage dynamic of the PV generator will face oscillations of power and frequency in PV-based microgrids. In this paper, to suppress the low-frequency oscillation issue caused by PV generations, an improved FCS-MPC (I-FCS-MPC) strategy considering the DC-link dynamics of PV generation is proposed, in which the AC side and DC side of the VSC are comprehensively considered to enhance the dynamic performance of the PV-based microgrids. Moreover, a DC-link voltage regulation mechanism is introduced, enabling the PV generators to operate in the maximum power point tracking (MPPT) mode. Finally, experiments are conducted to verify the effectiveness of the proposed control strategy comprehensively.

Adaptive centralized energy management algorithm for islanded bipolar DC microgrid

Abstract In this paper, an adaptive centralized energy management algorithm is developed for an islanded bipolar DC microgrid (BDCMG). The proposed microgrid consists of a solar photovoltaic (SPV) system, a wind energy conversion system (WECS), and a battery energy storage system (BESS). The SPV and WECS are operated in maximum power point tracking mode to extract maximum power. The adaptive control algorithm focus on power sharing at a bipolar DC bus based on variations in wind speed, solar irradiance, battery state of charge (SoC), and load. Furthermore, this algorithm maintains voltage balance and voltage regulation on a bipolar DC bus without the use of a communication link and provides effective energy management through coordinated control of renewable energy sources and BESS. The efficacy of the proposed bipolar DC microgrid is simulated and validated on a lab scale prototype to verify its feasibility, and it is observed that the results are found to be satisfactory.

Exploiting Photovoltaic Sources to Regulate Bus Voltage for DC Microgrids

DC microgrids are highly compatible with photovoltaic (PV) generation because of their direct-current properties. However, with the increasing integration of PV sources into DC microgrids, traditional maximum power point tracking (MPPT) algorithms may cause problems such as overvoltage and power fluctuation, which makes it challenging to keep the stability of the DC-bus voltage due to the intermittent and stochastic nature of PVs. Consequently, in order to reduce the investment and maintenance costs of storage systems, innovative control methods are required for PVs to provide DC-bus voltage regulation services. In this paper, a novel active power control (APC) strategy, based on characteristic curve fitting, is proposed to flexibly regulate the PV output power. The transient process performance and robustness of the system are improved with the proposed APC strategy. Based on it, a V-P droop mechanism is designed to provide voltage regulating (DVR) service for the DC microgrid. The overall control strategy unifies the DVR function with the traditional MPPT function in the same control structure; thus, the PV source either works in the MPPT mode if the DC-bus is at its nominal value, or it works in the DVR mode if the DC-bus exceeds it. Switching between MPPT and DVR is autonomous, and it is fully decentralized, which improves the PV generation efficiency as well as ensures generation fairness among different parallel PV sources. Case studies including a real-world project analysis are carried out to validate the feasibility and effectiveness of the proposed strategy.

Active power control strategy based on characteristic curve fitting for photovoltaic sources

AbstractWith the explosive growth of photovoltaic (PV) penetration, there is an increasingly urgent expectation for PV sources to provide a full range of ancillary services to the utility. The most critical issue for a PV source to support the power system is the flexible power regulating capability, whereas the PV sources generally operates in the maximum power point tracking (MPPT) mode to maximize the solar harvest. This paper proposes an active power control strategy for PV sources based on a characteristic curve fitting method. With the proposed control strategy, PV sources can operate in the MPPT mode or adaptively switch to the power dispatch mode to flexibly regulate its output power to track the power reference. The characteristic curve fitting method does not require an irradiation sensor, but it has a more stable performance under rapid and continuous change in irradiation with simple calculation and error control since its extremely high similarity with the P–U characteristics of the PV source. Simulation is performed based on a detailed PV dynamic model to validate the proposed control strategy.

A novel modeling and emergency control method of transient energy of DFIG-based wind turbine under grid fault

The anti-interference ability of the power system continues to decline with the increase of wind power penetration. Due to the limitation of the reduction of the synchronous generators proportion and the response speed, the emergency power controllability of the power system with large-scale wind power is insufficient, and the stability problems of the power system continue to highlight under the grid fault. At the same time, wind turbines can hardly provide emergency support for the power system due to the maximum power point tracking mode. Therefore, the transient energy of doubly fed induction generator based wind turbine (DFWT) during the grid fault is depicted, which is accumulated by the imbalance between the input mechanical power and output electromagnetic power. And a new idea of utilizing the fault transient energy of DFWT to provide emergency power support for the power system is proposed in this paper. Firstly, the expression of stator short-circuit current of DFWT in the crowbar input and reactive power support stage is derived. The dynamic model of the rotor speed of DFWT is established further, and the calculation method of fault transient energy of DFWT is proposed. Next, in order to maximize the utilization of the transient energy, the calculation method of control duration time and control reference value is proposed, and the transient energy emergency control (TEEC) method of DFWT is proposed. The case studies and experimental results show that the TEEC method can not only provide stable power support for the power system, but also alleviate the short-term power impulse of DFWT after the grid fault removal, and accelerate the recovery of the rotor speed of DFWT, which is conducive to the safe and stable operation of DFWT and the power system.

A comprehensive design method of an active electronic current transformer for stable energy harvesting from power lines

AbstractThe real‐time monitoring technology of power line is an important guarantee for the digitization and intelligence of the modern power system. Traditional current transformers cannot provide continuous and stable energy when the power line current fluctuates in a large range. This paper proposes a comprehensive design method of an active electronic current transformer (AECT) based on a single‐phase full‐bridge voltage‐type PWM rectifier (VSR), which can acquire continuous and stable energy from the power line with a minimum magnetic core. The control of the AECT is divided into four different operation modes to adapt the fluctuation of primary current. Maximum power point tracking (MPPT) mode and minimum loss (ML) mode is designed to harvest the maximum power with lower iron core loss when the primary current is relatively low. Excitation mode and demagnetization mode are designed to harvest stable power when the primary current is higher than a certain current. The specific mode operation principles and control strategies are analysed in detail. Besides, the calculation method of the primary current is also presented for the primary current measurement. Finally, simulations and experiments verify that the iron core can obtain power steadily (9.91 W/kg) when the current fluctuation between 50 and 600 A.

Battery state of charge based improved adaptive droop control for power management of a microgrid having large scale renewable generation

Power management in a standalone microgrid having large scale renewable generation is generally based upon droop control strategies. However, the renewable sources such as photovoltaics (PV), which are interfaced with microgrid through power electronic converters have low inertia. Because of low inertia, the transient power variation during switching events like change in load may be very large and may result into variation in voltage and frequency beyond permissible limits. In this paper an adaptive scheme for power management of a standalone microgrid is proposed, which is based on available state of charge (SoC) of the batteries to mitigate these issues. The proposed battery SoC based adaptive droop control for power management allows smooth transition of distributed energy resources (DERs) from maximum power point tracking (MPPT) mode to adaptive droop mode and vice versa. The transient behaviour is handled with the help of batteries and supercapacitor (SC), which is further improved with the help of fuzzy logic based proportional integral controller (FLC-PI). A significant drop in SC power during switching events is obtained with FLC-PI. The test microgrid is composed of multiple PV generation at different locations as primary source of generation, multiple batteries energy storage system (ESS) at different locations, fuel cell (FC), supercapacitor (SC) and a dynamic load.

Performances of a wind power system based on the doubly fed induction generator controlled by a multi-level inverter

The objective of this work is to study the contribution that the use of the multi-level inverter can make compared to a conventional two-level inverter, in a wind power production line (WPG) associated with a doubly fed induction generator (DFIG). The DFIG is driven by a variable speed wind turbine and operates in maximum power point tracking (MPPT) mode, for optimum efficiency. The rotor of the DFIG is supplied by a DC/AC inverter with five levels with MPC structure, controlled by the PWM technique, while the stator is connected with the network. The active and reactive powers exchanged between the DFIG and the network is achieved by indirect vector control with oriented stator flux (IFOC), with conventional regulators, ensuring zero reactive power and a unitary power factor. The total harmonic distortion (THD) of the current signals/voltages of the entire wind chain is exposed and criticized. The obtained results are very promising, offering the possibility for wind turbines with multi-level inverter to work in high voltage and large power.

Primary Frequency Regulation Strategy of PMSG-Based Wind Turbines Integrated Inertia Control

Direct-drive permanent magnet synchronous wind turbine-generator (PMSG) generally operates in maximum power point tracking (MPPT) mode. PMSG cannot provide additional active power support when the grid frequency fluctuates.The gradual increase of wind power penetration leads to greater frequency regulation pressure, which brings hidden dangers to the power grid. This paper focuses on the grid connection of direct-drive PMSG, proposing a primary frequency regulation strategy integrated with inertia control. The proposed strategy controls the rotor kinetic energy to simulate inertia of synchronous units by adding the support power of the response frequency variation. It concludes with variable power tracking control and energy storage system control. Compared with the traditional strategy retains active power, this proposed strategy increases wind energy utilization. Adopting variable power tracking control combined with energy storage can overcome secondary frequency drop and reduce energy storage configuration capacity.

Solar Maximum Power Point Tracking Algorithm Based on Wavelet Neural Network

With the development of human civilization, the shortage of resources and environmental pollution are increasing. Renewable energy is the only way to solve the problem of harmonious coexistence between human civilization and environment. More and more of them have come into our lives to reduce the burden of the earth and benefit mankind. Nowadays, due to the increasing gap of traditional power supply, solar energy has become a mainstream renewable energy, more and more applications, to provide us with green energy and improve our lives. The research on the application of solar energy system is of great academic value. This paper mainly studies the solar maximum power point tracking (MPPT) algorithm based on wavelet neural network (WNN). This paper analyzes several common maximum power tracking control methods, describes the structure design of WNN maximum power tracking control algorithm, and describes the structure and working principle of photovoltaic cells. In this paper, the output power of MPPT mode is analyzed and compared by simulating the use of solar photovoltaic system. In the meantime, the output voltage of solar charging panel in the charging process is studied. The experimental results show that the output power of the power supply in MPPT mode is kept at 0.2, and the output power of the solar panel is improved at every moment with the change of the external environment. At 9 o'clock, the output voltage of the solar panel is 3.6, and at 11 o'clock, the output voltage is 4.11.