An Adaptive fuzzy-PD inertial control strategy for a DFIG wind turbine for frequency support

With increasing levels of wind generation in power systems, guaranteeing continuous power and system’s safety is essential. Frequency control is critical which requires a supplementary inertial control strategy. Since wind power generation depends directly on wind conditions, this creates an immense challenge for a conventional inertial controller with parameters suitable for all power grid operations and wind speed conditions. Therefore, tuning the controller gains is absolutely critical for an integrated conventional/renewable power system. Here, a fuzzy-logic adaptive inertial controller scheme for online tuning of the proportional-derivative-type (PD) inertial controller parameters is proposed. The proposed controller adapts the control parameters of the supplementary inertial control of the doubly fed induction generator (DFIG) wind turbine so that with any disturbance such as load changes, the active power output can be controlled to mitigate the frequency deviation. Simulation results indicate that the proposed adaptive controller demonstrates a more consistent and robust response to load changes compared to a conventional controller with fixed parameters

Data-driven Virtual Inertia Control Method of Doubly Fed Wind Turbine

This paper presents a data-driven virtual inertia control method for doubly fed induction generator (DFIG)-based wind turbine to provide inertia support in the presence of frequency events. The Markov parameters of the system are first obtained by monitoring the grid frequency and system operation state. Then, a data-driven state observer is developed to evaluate the state vector of the optimal controller. Furthermore, the optimal controller of the inertia emulation system is developed through the closed solution of the differential Riccati equation. Moreover, a differential Riccati equation with self-correction capability is developed to enhance the anti-noise ability to reject noise interference in frequency measurement process. Finally, the simulation verification was performed in Matlab/Simulink to validate the effectiveness of the proposed control strategy. Simulation results showed that the proposed virtual inertia controller can adaptively tune control parameters online to provide transient inertia supports for the power grid by releasing the kinetic energy, so as to improve the robustness and anti-interference ability of the control system of the wind power system

Virtual Inertia: Current Trends and Future Directions

The modern power system is progressing from a synchronous machine-based system towards an inverter-dominated system, with large-scale penetration of renewable energy sources (RESs) like wind and photovoltaics. RES units today represent a major share of the generation, and the traditional approach of integrating them as grid following units can lead to frequency instability. Many researchers have pointed towards using inverters with virtual inertia control algorithms so that they appear as synchronous generators to the grid, maintaining and enhancing system stability. This paper presents a literature review of the current state-of-the-art of virtual inertia implementation techniques, and explores potential research directions and challenges. The major virtual inertia topologies are compared and classified. Through literature review and simulations of some selected topologies it has been shown that similar inertial response can be achieved by relating the parameters of these topologies through time constants and inertia constants, although the exact frequency dynamics may vary slightly. The suitability of a topology depends on system control architecture and desired level of detail in replication of the dynamics of synchronous generators. A discussion on the challenges and research directions points out several research needs, especially for systems level integration of virtual inertia systems

Control of Voltage-Source Converters Considering Virtual Inertia Dynamics

Controlling power-electronic converters in power systems has significantly gained more attention due to the rapid penetration of alternative energy sources. This growth in the depth of penetration also poses a threat to the frequency stability of modern power systems. Photovoltaic and wind power systems utilizing power-electronic converters without physical rotating masses, unlike traditional power generations, provide low inertia, resulting in frequency instability. Different research has developed the control aspects of power-electronic converters, offering many control strategies for different operation modes and enhancing the inertia of converter-based systems. The precise control algorithm that can improve the inertial response of converter-based systems in the power grid is called virtual inertia. This thesis employs a control methodology that mimics synchronous generators characteristics based on the swing equation of rotor dynamics to create virtual inertia. The models are also built under different cases, including grid-connected and islanded situations, using the swing equation with inner current and voltage outer loops. Analysis of the simulation results in MATLAB/Simulink demonstrates that active and reactive power are independently controlled under the grid-imposed mode, voltage and frequency are controlled under the islanded mode, and frequency stability of the system is enhanced by the virtual inertia emulation using swing equation. On this basis, it is recommended that the swing equation-based approach is incorporated with the current and voltage control loops to achieve better protection under over-current conditions. Further works are required to discover other factors that could improve the effectiveness of the models

Virtual Inertia Emulation to Improve Dynamic Frequency Stability of Low Inertia Microgrids

Due to low inertia and the intermittent nature of photovoltaic systems, dynamic frequency stability issues arise in microgrids with large photovoltaic systems. This limits the maximum amount of photovoltaic systems that can be penetrated in the microgrid. In order to increase the penetration of photovoltaic systems, the dynamic frequency controller, that is faster than the primary frequency controller (governor control) needs to be added in the microgrid system. For dynamic frequency control, inertial response can be provided from the energy storage system (such as battery, ultra-capacitor, photovoltaic system, etc.), which is termed as virtual inertia. A virtual inertia can be defined as the combination of an energy storage system, a power electronics converter and a proper control algorithm that improves the dynamic frequency stability of the microgrid. A virtual inertia supplies or absorbs the active power to and from the energy storage system to improve the dynamic frequency stability. This thesis presents the design and implementation of a hardware prototype of 1 kW virtual inertia in a microgrid with a real diesel generator and a load. For a step change in load, the virtual inertia improved the frequency response of the system from 57.39 Hz to 58.03 Hz. This improvement in frequency response proves the concept of existing proportional derivative based virtual inertia experimentally. With the addition of virtual inertia, the frequency of the system returns to the nominal frequency slower. Once the primary controller (governor control) of the system takes the action to regulate the frequency, virtual inertia no longer needs to add inertia to the system. So the dynamics of the VI needs to be improved so that the frequency returns to nominal frequency faster. This thesis also proposes an online learning controller based virtual inertia using adaptive dynamic programming that learns online and improves the dynamics of the controller of existing VI. The output of this controller supplements the output of the existing proportional derivative controller of virtual inertia. The supplementary controller is trained to increase the dynamics of the outer controller and to bring the system frequency to nominal frequency faster. Due to faster dynamics, the net energy delivered by the VI can be reduced significantly and improve the total possible discharge cycles from the battery. For performance evaluation, the proposed controller was implemented in a microgrid with a photovoltaic system, a diesel generator and a variable load. With the proposed controller, the frequency of the system returned to nominal frequency faster. The net energy delivered by the proposed controller in a photovoltaic diesel generator microgrid was 46.14% of the net energy delivered by the existing virtual inertia. Due to the decrement in the total energy delivered, the total number of possible battery discharge cycles with ADP based VI was 2.17 times of the total number of possible battery discharge cycles from VI

Optimization-based Fast-frequency Support in Low Inertia Power Systems

The future electrical energy demand will largely be met by non-synchronous renewable energy sources (RESs) in the form of photovoltaics and wind energy. The lack of inertial response from these non-synchronous, inverter-based generation in microgrids makes the system vulnerable to large rate-of-change-of-frequency (ROCOF) and frequency excursions. This can trigger under frequency load shedding and cause cascaded outages which may ultimately lead to total blackouts. To limit the ROCOF and the frequency excursions, fast-frequency support can be provided through appropriate control of energy storage systems (ESSs). For proper deployment of such fast-frequency control strategies, accurate information regarding the inertial response of the microgrid is required. In this dissertation, a moving horizon estimation (MHE)-based approach is first proposed for online estimation of inertia and damping constants of a low-inertia microgrid. The MHE also provides real estimates of the noisy frequency and ROCOF measurements. The estimates are employed by a model predictive control (MPC) algorithm that computes control actions to provide fast-frequency support by solving a finite-horizon, online optimization problem. The combined MHE-MPC framework allows an ESS operator to provide near-optimal fast-frequency support as a service. The framework maintains the desired quality-of-service (limiting the ROCOF and frequency) while taking into account the ESS lifetime and physical limits. Additionally, this approach avoids oscillatory behavior induced by delays that are common when using low pass filter and traditional derivative-based (virtual inertia) controllers with high gains. Through simulation results, it has been shown that the proposed framework can provide near-optimal fast-frequency support while incorporating the physical limits of the ESS. The MHE estimator provides accurate state and parameter estimates that help in improving the dynamic performance of the controller compared to traditional derivative-based controllers. Furthermore, the flexibility of the proposed approach to achieve desired system dynamics based on the desired quality-of-service has also been demonstrated

Application of Power Electronics Converters in Smart Grids and Renewable Energy Systems

This book focuses on the applications of Power Electronics Converters in smart grids and renewable energy systems. The topics covered include methods to CO2 emission control, schemes for electric vehicle charging, reliable renewable energy forecasting methods, and various power electronics converters. The converters include the quasi neutral point clamped inverter, MPPT algorithms, the bidirectional DC-DC converter, and the push–pull converter with a fuzzy logic controller

Intelligent Learning Control System Design Based on Adaptive Dynamic Programming

Adaptive dynamic programming (ADP) controller is a powerful neural network based control technique that has been investigated, designed, and tested in a wide range of applications for solving optimal control problems in complex systems. The performance of ADP controller is usually obtained by long training periods because the data usage efficiency is low as it discards the samples once used. Experience replay is a powerful technique showing potential to accelerate the training process of learning and control. However, its existing design can not be directly used for model-free ADP design, because it focuses on the forward temporal difference (TD) information (e.g., state-action pair) between the current time step and the future time step, and will need a model network for future information prediction. Uniform random sampling again used for experience replay, is not an efficient technique to learn. Prioritized experience replay (PER) presents important transitions more frequently and has proven to be efficient in the learning process. In order to solve long training periods of ADP controller, the first goal of this thesis is to avoid the usage of model network or identifier of the system. Specifically, the experience tuple is designed with one step backward state-action information and the TD can be achieved by a previous time step and a current time step. The proposed approach is tested for two case studies: cart-pole and triple-link pendulum balancing tasks. The proposed approach improved the required average trial to succeed by 26.5% for cart-pole and 43% for triple-link. The second goal of this thesis is to integrate the efficient learning capability of PER into ADP. The detailed theoretical analysis is presented in order to verify the stability of the proposed control technique. The proposed approach improved the required average trial to succeed compared to traditional ADP controller by 60.56% for cart-pole and 56.89% for triple-link balancing tasks. The final goal of this thesis is to validate ADP controller in smart grid to improve current control performance of virtual synchronous machine (VSM) at sudden load changes and a single line to ground fault and reduce harmonics in shunt active filters (SAF) during different loading conditions. The ADP controller produced the fastest response time, low overshoot and in general, the best performance in comparison to the traditional current controller. In SAF, ADP controller reduced total harmonic distortion (THD) of the source current by an average of 18.41% compared to a traditional current controller alone