Maximum Power Extraction Research Articles

Intelligent control of the power generation system using DSPACE

AbstractWind power systems (WPs) are complex non‐linear systems with varying parameters affected by environmental changes, including wind speed fluctuations. Extracting maximum power from WPs poses a significant challenge due to these factors. Direct power control (DPC) is a highly effective technique known for its simplicity and ease of implementation. However, it suffers from power ripples caused by the use of hysteresis comparators and switching tables that operate at variable frequencies. To address this issue, this paper presents the robust neural controller (NC) based on DPC, which replaces the switching tables. The Double‐Fed Induction Generator (DFIG) is the chosen generator for the studied WP system due to its advantageous features. The NC‐DPC effectively regulates the exchange of active and reactive powers between the DFIG and the system, maximizing power extraction from the WP system while reducing Total Harmonic Distortion and enhancing overall system quality. The effectiveness of the NC‐DPC is evaluated through MATLAB simulations and further supported by experimental data obtained using the Real‐Time Interface of the dSPACE‐DS1104 Controller card.

DFIG in Wind Energy Applications with High Order Sliding Mode Observer-based Fault-Tolerant Control Scheme using Sea Gull Optimization

This paper describes a new method for maximizing power extraction from a wind energy conversion system (WECS) by using a doubly fed induction generator (DFIG) that operates below nominal wind speed. To maximize the collected power of a wind turbine (WTG) exposed to actuator failure, a fault-tolerant high-order sliding mode observer (HOSMO) and Seagull Optimization Algorithm with a model predictive controller (MPC) technique is proposed. Evaluate both the real state and the sensor error simultaneously using a higher-order sliding-mode observer. Active fault tolerant controllers are designed to regulate wind turbine rotor speed and power in the presence of actuator defects and uncertainty. With the growing interest in employing wind turbines (WTGs) as the primary generators of electrical energy, fault tolerance has been seen as essential to improving efficiency and reliability. This research focuses on optimal fault-tolerant pitch control, which is used to modify the pitch angle of wind turbine blades in the event of sensor, actuator, and system failures. A Seagull Optimization Algorithm (SOA) is proposed to tune controller parameters to improve the performance of WT. The proposed method has achieved 92% of power tracking performance when compared to existing method.

Analysis of deep learning models for estimation of MPP and extraction of maximum power from hybrid PV-TEG: A step towards cleaner energy production

The Hybrid Photovoltaic-Thermoelectric generation system (PV-TEG) exhibits immense potential for effectively harnessing solar energy. However, the overall effectiveness of the hybrid PV-TEG system is hindered by the relatively low efficiency of both PV and thermoelectric generation (TEG). To overcome this limitation, this research investigates the application of an evolutionary Neural Network (NN)-based Maximum Power Point Tracking (MPPT) control technique. This proposed control technique utilizes a Growth Optimizer Algorithm (GO-algorithm) to optimize the weights and biases of a Deep Neural Network (DNN), enabling real-time tracking of the global maximum power point (GMM). Furthermore, to fortify the robustness of the MPPT control, a modified Fractional Order Proportional-Integral-Derivative (FOPID) controller is used whose gains are tuned using the GO-algorithm (GO-FOPID). Experimental evaluations conclusively demonstrate the efficacy of the evolutionary NN-based MPPT control technique in enhancing the generation efficiency of hybrid PV-TEG systems. The effectiveness of the proposed MPPT technique is validated through a comparative analysis with other MPPT techniques. The GO-DNN-based MPPT controller excels in its real-time global maxima tracking capability, exhibiting minimal power oscillations with average efficiency of 99.928% and an exceptionally low tracking time of less than 5ms. This superior performance is verified by comparative, simulation, quantitative, and statistical analyses, thereby highlighting the efficiency, tracking time, stability, and fault detection capabilities of the GO-DNN and GO-FOPID controllers across various practical scenarios.

Design of novel differential power processing scheme for small-scale PV array application operating under partial shading conditions: modelling and experimental validation

In this study, an innovative approach known as Series and Parallel Differential Power Processing (SP-DPP) converters has been introduced and applied to a small-scale 2 × 2 PV array configuration. Unlike conventional methods, this approach employs Bidirectional Ćuk DC-DC Converters (BCC) in lieu of bypass diodes within serially connected PV modules, facilitating precise adjustment of module currents. Additionally, the Differential Power Processing (DPP) mechanism, utilising an Inverted-Buck Converter (IBC) topology, replaces traditional blocking diodes across parallel string connections. The paper develops mathematical models for these converters based on their transfer functions in the Continuous Conduction Mode (CCM), establishing new design criteria for SP-DPP systems. This approach ensures a well-behaved transient response and mitigates non-minimum phase response effects. Empirical testing demonstrates the efficacy of the proposed system under various static and dynamic partial shading conditions (PSCs). Furthermore, a model-based control strategy employing P + I controllers are formulated and experimentally validated to optimise the operating conditions of PV modules, enabling them to operate at their Maximum Power Points (MPPs) in varying lighting conditions. Through simulation and experimental results, the study affirms a substantial increase in the total output power of the SP-DPP system (approximately 37–41 %) compared to conventional Tied-Crossties (TCT) array configuration relying solely on bypass and blocking diodes under various static shading conditions, such as Short-Narrow (SN), Short-Wide (SW), Long-Narrow (LN) and Long-Wide (LW). Ultimately, this research highlights the novelty of the proposed SP-DPP scheme over other existing DPP schemes in the literature, in terms of maximum power extraction, cost and complexity under various lighting conditions. This research extends to explore the potential economic viability and global implications of large-scale DPP system implementation.

Control co-design optimization of nonlinear wave energy converters

This paper presents a study in which both the control and the shape of a Wave Energy Converter (WEC) are optimized simultaneously. A heaving point absorber WEC is assumed. To optimize the shape of the WEC’s buoy, nonlinear hydrodynamics need to be evaluated. One main contribution of this paper is the integration of nonlinear hydrodynamics and nonlinear control during the optimization of the WEC’s buoy shape. This approach is referred to as Control Co-Design (CCD). In this work, we present a control co-designed nonlinear heaving point absorber WEC that leverages the nonlinear dynamic, static Froude–Krylov (FK) forces to maximize power extraction. The nonlinear FK forces are approximated using a variation of the algebraic solution; the hydrodynamic forces of the body are computed using an analytic formulation leveraging the methods of eigenfunction expansion and separation of variables. The nonlinear geometry of the buoy is modeled as a series of inclined panels; the inclination angles are optimized to arrive at the optimal shape. The performance of the optimized shape is compared to that of a nonlinear spherical WEC. It is found that an average of 20% improvement is achieved by the optimized geometry over the spherical device.

A Novel Design and Analysis Adaptive Hybrid ANFIS MPPT Controller for PEMFC-Fed EV Systems

Now, the present electric vehicle industry is focusing on the fuel cell technology because its features are high flexibility, continuous power supply, less atmospheric pollution, fast startup, and rapid response. However, the fuel cell gives nonlinear power versus current characteristics. Due to this nonlinear behavior, the maximum power extraction from the fuel stack is quite difficult. So, in this work, an adaptive genetic algorithm with an adaptive neuro-fuzzy inference system (ACS with ANFIS) MPPT controller is introduced for finding the MPP of the fuel stack system thereby extracting the peak power from the fuel stack. The proposed hybrid maximum power point tracking (MPPT) controller is compared with the other MPPT controllers which are enhanced incremental conductance-fuzzy logic controller (EIC with FLC), improved hill climb with fuzzy logic controller (IHC with FLC), adaptive beta with FLC, enhanced differential evolutionary with FLC (EDE with FLC), and marine predators optimization with FLC (MPO with FLC). Here, these hybrid controllers’ comprehensive investigations have been carried out in terms of tracking speed of the MPP, oscillations across the MPP, settling time of the converter voltage, maximum power extraction from the fuel stack, and working efficiency of the MPPT controller. The fuel stack generates a very low output voltage which is improved by using the boost DC-DC converter, and the overall fuel stack-fed boost converter system is designed by utilizing the MATLAB/Simulink tool. From the simulation results, the AGA with ANFIS MPPT controller gives high MPP tracking efficiency when compared to the other hybrid controller.

Improved solar photovoltaic performance in standalone low‐voltage direct current microgrids using sensor fault tolerant control

AbstractThe advancement of renewable energy technology has been significantly aided by solar photovoltaics (PV). Since solar PV is a weather‐dependent source, it cannot be dispatched. To ensure that the solar PV system can harvest the maximum amount of electricity for the available irradiance level, maximum power point tracking (MPPT) algorithms are used. For standalone low‐voltage DC (LVDC) microgrids to utilize the energy storage system as efficiently as possible, maximum power extraction is essential. The sensed PV voltage and current are essential for these MPPT algorithms to ensure that the maximum power point of the panel is captured. This work proposes an effective fault‐tolerant control (FTC) scheme for the solar PV subsystem in the LVDC microgrid that can seamlessly extract the maximum power despite the PV voltage sensor being faulty. The proposed FTC scheme uses a sliding mode observer (SMO)‐based method to detect and isolate PV voltage sensor faults in the standalone LVDC microgrid. The efficacy of the proposed FTC is assessed in a range of circumstances involving load disturbance, irradiance change, and various sensor fault scenarios. The performance of the proposed FTC is validated using experimental analysis on the LVDC microgrid testbed and MATLAB simulations. Given a faulty PV voltage sensor, at a given operating condition of the microgrid, the proposed FTC scheme is successful in reducing the additional power burden on the battery storage by at least two times. Consequently, the additional discharge in terms of SoC is also seen to be decreased by at least 9%. The proposed FTC technique outperforms the popular MPPT approaches for solar PV in terms of PV voltage sensor fault tolerance in the microgrid.

Performance enhancement of a three-phase grid-connected PV inverter system using fractional-order integral sliding mode controls

Grid-integrated photovoltaic (PV) systems hold significant promise for sustainable energy production. However, these systems often struggle with maintaining energy quality and stability in the face of fluctuating conditions. To address these challenges, this study proposes the use of fractional-order integral sliding mode control (FO-ISMC) for grid-connected PV systems. The system comprises solar panel arrays, a DC/DC boost converter with its controller, and a three-phase inverter integrated into the utility grid. The primary goals of this study are to maximize power extraction from the PV system and to regulate system currents, thereby improving effectiveness and power quality. The proposed method is further refined using the particle swarm optimization (PSO) algorithm to fine-tune parameter settings. By optimizing the command gains of the FOI-SMC, the PSO algorithm enhances system performance and stability. Leveraging fractional-order and integral terms provides the control signal with additional degrees of freedom, enhancing system performance in the presence of internal and external disturbances. Simulation results demonstrate the effectiveness of the proposed controller compared to the classical controller, where in the case of Irradiation taking the value of 500 W/m2, the proposed strategy reduced the values of constant voltage, active power, and reactive power by percentages estimated at 88.18%, 90%, and 70%, respectively, compared to the traditional strategy. Also, the overshoot value of active power was reduced in all completed cases compared to the traditional strategy by percentages estimated at 53.57%, 66.66%, and 65%, respectively. The proposed strategy reduced the response time of reactive power in all cases compared to the traditional strategy by percentages estimated at 58.59%, 28.80%, and 75%, respectively. These ratios show the high performance of the proposed control in improving the characteristics of the studied system compared to traditional control based on PI control.

Investigation and Reduction of Harmonic in Grid Connected PV Fed DSTATCOM System

The utilization of a photovoltaic-based distribution static compensator (PV-DSTATCOM) stands out as a prominent solution for addressing energy demand deficits and power quality challenges within contemporary power systems. This article focuses on enhancing the performance of PV-DSTATCOM to facilitate grid integration and elevate power quality standards. In the envisioned system, the power from the photovoltaic array is harnessed through the utilization of the sliding mode control, ensuring the extraction of maximum power. The performance of the PV-DSTATCOM is analyzed by using a Packed U cell 5 inverter. The control signals for the voltage-source inverter are generated using PQ control scheme. The efficacy of the proposed system is substantiated through MATLAB simulation results. The outcomes presented in this article highlight the accomplished improvement in the performance of PV-DSTATCOM, ensuring concurrent advancements in current THD.

Increased power capture efficiency of large-scale wind turbines using model-free coordinated pitch, yaw, and torque control with wind direction estimation in diverse environmental conditions

Offshore large-scale wind turbine systems (LSWTS) face challenges in pitch and yaw management mainly due to unreliable wind direction measurements, delayed responses, and redundant yaw movements, reducing their optimal efficiency under various offshore renewable environmental conditions. To address this problem and increase power capture, this study introduces a model-free coordinated super-twisting pitch, yaw, and generator torque control for LSWTS under various wind speed and direction conditions. To achieve this, a dynamic model of the weather vane is first designed to accurately measure the wind directions under fluctuating wind speed and environmental conditions. At the same time, the onshore/offshore LSWTS weather vane model is formulated to operate under optimal conditions considering air density, temperature, humidity, and external disturbances. Then, super-twisting control based on an enhanced adaptive barrier function is proposed for the coordinated control operation of the pitch, yaw, and generator torque actuators for stable maximum power extraction of the offshore LSWTS. Finally, simulation and comparative studies are presented to signify the increased power capture ability of the proposed scheme for both a 4.8 MW benchmark and a 20 MW PMVG-based offshore LSWTS to verify robustness against wind gusts, direction changes, and model uncertainties.

Fast DC-link voltage control based on power flow management using linear ADRC combined with hybrid salp particle swarm algorithm for PV/wind energy conversion system

Hybrid Energy Systems (HESs) are offering several advantages over conventional generation systems because of the rising need for Renewable Energy Resources (RERs) and its high capability and flexibility of achieving the desired performance for both urban and rural areas. The main goals of HESs are to improve the system performance by increasing its flexibility, reliability and reducing generation instability. In the current work, a hybrid Photovoltaic/Wind Energy Conversion System (PV/WECS) is proposed to supply and support the utility grid. The generated power from the system is greatly responding to the change in both the wind speed and PV irradiance level, and as a result impacting the DC-Bus voltage of the system converters. Therefore, precise, fast and optimum controllers are required for the PV/WECS system to ensure the extraction and generation of stable maximum power during weather variations along with maintaining the DC-Link voltage at the desired value. Further, this study proposes a novel Linear Active Disturbance Rejection Control (LADRC) combined with a hybrid Salp Particle Swarm Algorithm (SPSA) to a grid-connected PV/WECS for extracting the Global Maximum Power (GMP), regulating the DC-Link voltage, and enhancing the active and reactive powers control. The capability of the proposed SPSA-LADRC based-MPPT approach is examined according to the tracking time, accuracy and response to internal and external disturbances and then comparing its performance with other techniques such as SPSA-SMC, SPSA-PID and SPSA-PI controllers respectively. The findings revealed that the LADRC can track and keep the DC-Link voltage as the desired reference value,1500 V, respond much faster than the PI, SMC, and PID controllers as well as keep the THD of the injected current into the grid at a low level of 2.25%. Finally, the performance of the proposed system is experimentally verified using Hardware-In-Loop (HIL). The obtained findings verify the capability and accuracy of the system and its great potential in the management of HESs.

The Ability of Convergent–Divergent Diffusers for Wind Turbines to Exploit Yawed Flows on Moderate-to-High-Slope Hills

Small-to-medium-sized wind turbines operate with wind speeds that are often modest, and it is therefore essential to exploit all possible means to concentrate the wind and thus increase the power extracted. The advantage that can be achieved by positioning the turbine on hilly reliefs, which act as natural diffusers, is well known, and some recent studies can be found on the effects of the characteristics of hilly terrain on the turbine performance. The literature shows numerous investigations on the behavior of ducted wind turbines, i.e., equipped with a diffuser. But so far, there is a lack of studies on the flow acceleration effects achievable by combining natural relief and a diffuser together. In this study, we analyze the performance of a 50 kW ducted turbine positioned on the top of hills of various shapes and slopes, with the aim of identifying the geometric characteristics of the diffuser most suitable for maximizing power extraction. The results show that a symmetrical convergent–divergent diffuser is well suited to exploit winds skewed by the slope of the hill, and therefore characterized by significant vertical velocity components. Due to its important convergent section, the diffuser is able to convey and realign the flow in the direction of the turbine axis. However, the thrust on the diffuser and therefore on the entire system increases dramatically, as does the turbulence released downwind.

Design of Buck Converter Based on Maximum Power Point Tracking for Photovoltaic Applications

e MPPT converter ensures that the PV system operates at the maximum power point, which is the point where the solar panels can generate the most power. This is done by adjusting the voltage of the output. The converter uses a DC-DC conversion process and can be implemented using a buck converter circuit. This project uses a buck converter to adapt the voltage to its appropriate value to reach a maximal power extraction. This power converter can be designed in several ways. This involves employing a typical power converter design to create the power converter in the MPPT converter. Furthermore, the inductance and capacitance derivation for the MPPT converter is insufficient, making determining the inductance and capacitance unfeasible. Hence, this study focuses on designing the buck converter for MPPT application and tracking the maximum power using the Perturb and Observe (P&O) method. This system will be implemented by using MATLAB/Simulink. To design the MPPT buck converter, several parameters need to be considered and derived. This project finds that the MPPT buck converter that has been designed can track the maximum power from 900 W/m2 to 1100 W/m2 of irradiance. The design of the MPPT buck converter using the P&O method is fairly accurate, and the accuracy of tracking the maximum power is around 98%.

A new MPPT controller based on a modified multiswarm PSO algorithm using an adaptive factor selection strategy for partially shaded PV systems

Maximum power point tracking (MPPT) controller is the main element in photovoltaic (PV) systems, which is used to ensure maximum power extraction under different meteorological conditions. A MPPT controller can guarantee good performance criteria even in the presence of climatic changes. To achieve this goal, several techniques have been proposed in the literature to improve robustness of the PV system control, such as artificial intelligence and multiswarm particle swarm optimization (MSPSO) algorithm. Previous research on classical MSPSO has shown that the algorithm search behavior cannot find the optimal solution for certain problems. In this context, we investigate the design of a new MPPT controller based on a modified version of heterogeneous multiswarm particle swarm optimization algorithm using an adaptive factor selection strategy (FMSPSO) for PV systems. The proposed FMSPSO can improve the tracking capability with high accuracy, less oscillations, and high robustness. To validate the proposed solution, a simulation and experimental benchmarking of a PV system are presented and analyzed. The obtained results show the effectiveness of the proposed solution compared with the classical MSPSO, fuzzy logic, and perturb and observe (P&O) control presented in other recent works.

A smart and intelligence based controlling algorithms for maximum power extraction and effective battery management in EV systems

AbstractAn economically appealing alternative to the use of fossil fuels in many nations is the electric vehicle (EV). Commercial EVs have a battery that powers the motor while also being charged by solar photovoltaic (PV). The original contribution of this research work is to implement a smart and advanced methodologies for accomplishing the objectives of MPPT control and battery management in EVs. Here, a novel spotted hyena optimized (SHO)‐MPPT controlling algorithm is applied to get the most out of the electricity generated by PV panels to meet EV energy requirements. A modified high gain super‐lift Luo converter has been implemented to improve and regulate the output voltage of PV for optimal battery charging and motor control. Then, an adaptive dynamic encoded network controller is designed to improve the voltage regulation and boosting performance of the DC‐DC converter. Consequently, a Henry gas solubility optimization is deployed to perform an effective battery management in EVs by optimally estimating the state of charge, which helps to increase the charging efficiency of EVs. Moreover, a three‐phase voltage source inverter has also been used to lower harmonic levels and deliver high‐quality output power to the EV motor. The results of the proposed MPPT controlling, converter controlling, and battery management strategies are verified in this study using a complete simulation and comparison analysis, where the parameters such as IV‐PV characteristics, motor torque, speed, total harmonic distortions (THDs), and so forth are taken into account for performance validation. By using the proposed controlling methodology, the THD is reduced to 1.09% with the improved vehicle speed of 3000 rpm, and overall performance efficiency of 99%.

Binary firefly algorithm based reconfiguration for maximum power extraction under partial shading and machine learning approach for fault detection in solar PV arrays

The functioning of a photovoltaic (PV) array in the shadow presents significant issues owing to power loss, which affects the harvested power. One of the greatest promising methods for reducing the effect of shadow over the array is reconfiguration. Therefore, this work proposes a modern metaheuristic Binary Firefly Algorithm (BFA) technique for solving the reconfiguration mechanism of a partial shade PV array. The proposed BFA is tested on a 9 × 9 panel PV array with four established shadow arrangements: short wide (SW), long wide (LW), short narrow (SN), and long narrow (LN). The proposed BFA approach yields configurations superior when compared to existing reconfigurations. The best GMP boost obtained with the proposed BFA concerning TCT configuration is 36% in the SW shadow pattern, 30 in the LW arrangement, while the least is 7% in the SN and LN patterns. To evaluate the ability of the proposed system, it is compared with other reconfiguration methods tested across various performance metrics such as fill factor, mismatch power loss, percentage of power loss, and power enhancement. The energy estimates and revenue generation demonstrate that the proposed BFA approach generates 15% more power than the TCT setup and other methods. Furthermore, the Nave Bayes based Machine Learning (ML) approach is applied to detect physical degradation in PV panels. To validate the performance proposed ML method and other techniques are undergone with both faulty and non-faulty conditions. The output of the proposed ML technique-based identification of faults approach is compared to existing techniques. The acquired results supported the proposed BFA with ML's ability and superiority in optimally reconfiguring the shaded array.

Improved power quality based PVDG system using different optimised MPPT controllers

The characteristic curve of photovoltaic (PV) modules is nonlinear due to environmental influences. The maximum quantity of energy can only be taken from the nonlinear angle of the solar system at one exact moment. The primary function of an MPPT (maximum power point tracking) controller in a PV system is to extract maximum power point (MPP) from solar panels. In this paper, an enhanced MPPT approach using different optimization techniques is suggested for PV systems. The Fuzzy logic controller (FLC), Whale Optimization Algorithm(WOA), and Modified Grey Wolf Optimization algorithm (MGWO) are presented for the MPPT controller. Among all methods, the MGWO technique is one of the best at determining the MPP in solar systems because of its quick response and low fluctuations. Accurate training data is one of the biggest challenges in developing an effective MGWO. The irradiance and temperature are taken into consideration as input variables, and the optimal voltages are optimized as an output variable using the MGWO algorithm. Matlab/Simulink simulations are run to verify the suggested model’s suggested tracking effectiveness. To ensure accurate results, simulations are run under various climatic conditions. The simulation result shows that the proposed method operates correctly in a variety of climatic conditions efficiently.

Maximum Power Point Extraction in PV Array Under Partial Shading Conditions Using GWO-Assisted Beta Method

This paper presents a hybrid maximum power point tracking (MPPT) algorithm, which combines the grey wolf optimization (GWO) technique and Beta method for extraction of maximum power from a photovoltaic (PV) system operating under partial shading conditions. The proposed algorithm named GWO-Beta-based MPPT uses the GWO technique to achieve the global maximum power point (GMPP), while, simultaneously, the MPPT reference using Beta method is calculated according to the PV voltage and current at GMPP. After that, the MPPT algorithm is switched to operate only with the Beta method that keeps tracked on the MPP reference, guarantying the GMPP in that specific operation point. By means of computational simulation results, the performance of the proposed MPPT algorithm is evaluated taking into account the convergence speed and power oscillations to achieve the GMPP.

Smart Grid Operation with Hybrid Renewable Resources and Electric Vehicle

The utilization of renewable energy resources will significantly increase to achieve clean and sustainable electricity generation. The management of the continuous growth share of variable renewable resources, integration of electric vehicles (EVs) and regulation of grid frequency require effective communication facilities of recent smart grids. In this respect, smart converters can provide efficient power conditioning devices to extract maximum power from renewable resources. In addition, the batteries of EVs could be used as distributed storage and discharge power back into the grid to compensate power deficit and flactuation. Modeling and controlling of the hybrid resources within smart grid were carried out in this paper. The smart converter is switched to track the maximum power point MPPT of the solar panels. In addition, the pitch angle control and DFIG regulation are used to extracte the maximum power under different wind speed and tidal stream velocities. The deployment of EVs can compensate power fluctuations and regulate the frequency deviations in the grid based on the proposed PID controller. In this paper, the integral minimization of time-weighted absolute error (ITAE) is used for on-line tuning of the PID controller parameters. Using Matlab/Simulink package, the smart grid is modeled and simulated using the weather data of Kuwait and then the grid performance is assessed under system disturbances and load excursions.

Coordination of synthetic inertia from wind turbines and battery energy storage systems to mitigate the impact of the synthetic inertia speed-recovery period

It is well-known that wind power plants can provide short-term frequency control responses; however, when wind turbines are operated to extract maximum power from the wind, any additional energy supplied to the grid in response to the frequency event must be returned to the wind turbine to accelerate it back to optimum speed. The wind turbine must temporarily lower its power output to facilitate acceleration back to optimum speed. In power systems that have many wind turbines providing short-term frequency control services, the reduction in power generation associated with optimum speed recovery could be significant enough to cause a secondary frequency dip shortly after the initial dip, the nadir of which could be lower than the initial dip. This paper proposes 1) an improved speed-recovery scheme for wind turbines that provide synthetic inertia, and 2) a coordinated control scheme for wind turbines and BESSs to mitigate the secondary frequency dip that can occur during the synthetic inertia speed-recovery phase. The control schemes presented can be easily integrated into existing wind turbine and BESS active power controllers as implementation can be achieved using only the active power and rotational speed measurements that are available in the wind turbine's active power controller.