Frequency Control Research Articles

Research on Frequency Control Technology in Electrical Automation Control

With the continuous improvement of production efficiency, quality and flexibility, electrical automation control technology plays an increasingly important role in industrial production. As the core technology of electrical automation control, frequency conversion speed regulation is of great significance to improve production efficiency, reduce production costs, ensure stable operation of equipment and reduce energy consumption of electrical automation control system. Frequency conversion speed regulation refers to the frequency and voltage adjustment of motor power supply, so as to achieve accurate control of motor speed. The economy of this technology is mainly manifested in its ability to effectively reduce production costs, while its versatility is mainly manifested in its high degree of functional modularity and wide adaptability. Simply put, frequency conversion speed control technology is essential in the electrical automation control system. It reflects the scientific and technological progress of the enterprise, and also plays a decisive role in the enhancement of enterprise benefits. This paper provides a more comprehensive discussion on the application of frequency conversion speed control technology in electrical automation control for relevant personnel.

A heuristic-fuzzy improved virtual synchronous generator control strategy for charging station frequency regulation

To make use of charging station (CS) frequency regulation (FR) capability to get a better FR effect, a CS virtual synchronous generator (VSG) frequency control strategy based on the heuristic-fuzzy logic controller (FLC) is proposed in this paper. First, a CS VSG control architecture with heuristic-FLC is designed. Second, considering electric vehicle (EV) users' charging demands, a CS FR capability evaluation model based on the constant current-constant voltage (CC-CV) charging process and a V2G power response model based on frequency up/down-regulation participation factor are designed. Furthermore, a heuristic-FLC optimized by genetic algorithm (GA) is designed to improve the FR effect of the VSG control strategy. Simulation results show that the proposed strategy can make full use of the CS FR capability and get a better FR effect. In addition, the V2G power response model proposed in this paper can achieve the orderly responses of EVs in a CS under different scenarios.

Resilient robust model predictive load frequency control for smart grids with air conditioning loads

AbstractThis paper investigates the robust model predictive load frequency control problem for smart grids with wind power under cyber attacks. To accommodate intermittent power generation, the demand response of the system is considered by involving the air conditioning loads in the frequency regulation. In addition, the system uncertainties produced by the air conditioning load users and wind turbines when replacing traditional generator sets are considered. By using the cone complementary linearization algorithm and the linear matrix inequality technique, a resilience robust model predictive control strategy with mixed performance indexes is proposed. Furthermore, a rigorous derivation of the recursive feasibility of robust model predictive control is given. Finally, the simulation results of the two‐area load frequency control scheme show that the proposed model predictive control strategy is capable of realizing the load frequency control of the multi‐area smart grid and is robust to the parameter uncertainties and frequency regulation of the system. The results also show that the proposed model predictive control strategy has some resistance to cyber attacks and external disturbances.

Load frequency control of uncertain power systems via a novel worst-case GADRC scheme

This article proposes a worst-case generalized active disturbance rejection control (WGADRC) technique for power systems with parametric uncertainties. The proposed scheme is based on the worst-case plant selection strategy. Firstly, a worst-case model of the plant is identified out of the various possible models of an uncertain system, and afterward, the GADRC scheme is employed to design a controller. The best feature of the proposed scheme is that the designed controller ensures superior performance under nominal as well as uncertain plant conditions than other control schemes in the existing literature. To authenticate the supremacy of the proposed methodology, the load frequency control (LFC) issues associated with numerous power systems such as single-area thermal power system, IEEE 39 bus 10 machine test power system, and higher order hydro-thermal power system involving unmodeled parametric uncertainties, unexpected nonlinearities, and physical discrepancies are considered.

2DOF-PID-TD: A new hybrid control approach of load frequency control in an interconnected thermal-hydro power system

The Load Frequency Control (LFC) scheme, with its primary aim being the maintenance of uniform frequency, has been a heavily researched topic for decades. Achieving a consistent frequency necessitates a delicate balance between load demand and power generation. Researchers strive to find an optimal solution within the LFC domain—one that can effectively withstand drastic load fluctuations. Despite a plethora of efforts, the LFC dilemma remains unresolved, complicated by factors such as dwindling demand-supply and the rapid integration of renewables. Furthermore, the lack of innovation in controller structure design exacerbates the complexity of solving modern LFC problems. Consequently, a robust control approach capable of handling uncertainties while simultaneously regulating system frequency becomes crucial. In light of this, we propose a novel hybrid control architecture called 2DOF-PID-TD. This architecture combines Two Degrees Of Freedom Proportional-Integral-Derivative (2DOF-PID) and Tilt-Derivative (TD) controllers. To optimize the proposed controller, we employ a metaheuristic called the Artificial Gorilla Troops Optimizer (AGTO), which mimics the social behavior and intelligence of gorilla troops. The proposed approach is analyzed in a realistic multi-area multi-source hydro-thermal system, accounting for nonlinearities, random load perturbations, and system parametric uncertainties. Experimental results, when compared with current state-of-the-art optimization algorithms and traditional controller structures, demonstrate the prowess of our approach in terms of precision, robustness, and resilience.

Human-in-the-loop control of dynamics and robotics using augmented reality

Human-machine interaction (HMI) and human-robot interaction (HRI) offer innovative approaches for the analysis of structural dynamics both in laboratory and real-world scenarios. In vibratory experimentation, an external force is generated to evaluate dynamic responses of structures. One mode of generation is to use electrodynamic shakers. Sensors are attached to the structure to measure dynamic responses and can be deployed by robots. In post-disaster inspections robotic deployment ensures the safety of inspectors and requires control. If the interface between operators and the controls was augmented, then operators can visualize experiments, exciter levels, and define robot input while maintaining awareness of the area of interest. Robots can provide better aid to humans if control of the robot is: (1) quantified and presented to the human; and (2) conducted in real-time for human feedback. This research proposes using Augmented Reality (AR) to provide operators with sensor feedback and control for analysis of dynamic structures with robots. This method improves cognition by allowing the operator to maintain awareness of structures while adjusting conditions accordingly with the assistance of the new real-time interface. One interface application is developed to plot sensor data in addition to voltage, frequency, and duration controls for vibration simulations. Two more applications are developed following the same framework, one to control the position of a mediating robot and one to control the frequency of the robot's movement. The applications serve as a first-step approach investigating potential robotic sensor deployment in disaster assessment of structures. This paper presents the proposed model for the new vibratory control loop and then compares the new approach with a traditional method by measuring time delay in control input. The study also demonstrates that AR enhances time efficiency and accuracy in controlling robotic arm positions, matching actuator frequencies, and deploying sensors with minimal time delay.

A synthetic moving-envelope metasurface antenna for independent control of arbitrary harmonic orders

Flexible frequency controls are crucial in many photonic and electronic applications, ranging from communications systems, spectroscopy, and metrology to quantum information processing. However, the state-of-the-art solutions based on nonlinear bulk media, electro-optic effect, and nonlinear metasurfaces incur very limited spectral controllability, and merely a couple of harmonic orders can be independently manipulated. Here, we theoretically propose and experimentally demonstrate synthetic moving-envelope metasurface antennas capable of simultaneously generating arbitrary harmonic orders and independently manipulating their wave properties in a software-defined manner. As proof-of-principle examples, we demonstrate unidirectional frequency transition, frequency comb generation, arbitrary harmonic orders independent control, and their applications in frequency-division multiplexing communications. All these complicated functionalities are achieved by the 1-bit spatiotemporally ON-OFF switching of meta-atoms of the waveguide-integrated metasurface antenna. Our proposed synthetic metasurface antenna solution greatly expands the frontiers of wave engineering and information manipulation, showing promising potential in wireless communications, spectroscopy, metrology, and quantum science.

The effect of the MBL2 gene rs1800450 variant on COVID-19 development in Turkish patients

The coronavirus disease 2019 (COVID-19) is a recent pandemic occurring worldwide due to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, spreading mainly through large respiratory droplets or maybe through other transmission routes. The human genome has the most varied immune response genes correlated with infectious diseases. Genetic variants of mannose-binding lectin 2 (MBL2), an immunomodulatory gene, were associated with the risk, severity, and frequency of viral infections. In the present study, we hypothesized that the MBL2 gene rs1800450 variant could be associated with the development of COVID-19 disease in a Turkish population. Ninety-eight COVID-19 patients and 98 healthy, ethnically matched controls were studied. We isolated genomic DNA from whole blood and analyzed the MBL2 rs1800450 using the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method. Associations were analyzed with the SPSS 20 statistical software. We found that MBL2 rs1800450 genotype distribution was significantly different between patients and controls. The patients had a higher MBL2 rs1800450 AA genotype than the controls had (4.94% in patients vs. 3.12% in controls, p = 0.006). The subjects carrying AA genotype had a 10.83-fold increased risk for COVID-19 disease (OR = 10.83, %95 CI = 1.359–86.349). We could not detect any significant difference between the COVID-19 patients and healthy controls in allele frequencies. Our findings demonstrated that the MBL2 rs1800450 BB genotype might increase the susceptibility to COVID-19 disease in the Turkish population. We suggest further studies with a larger sample size and other ethnic populations.

Different time courses of maturation for learning and generalization following auditory training in children

Objective We recently demonstrated that learning abilities among school-age children vary following frequency discrimination (FD) training, with some exhibiting mature adult-like learning while others performing poorly (non-adult-like learners). This study tested the hypothesis that children’s post-training generalisation is related to their learning maturity. Additionally, it investigated how training duration influences children’s generalisation, considering the observed decrease with increased training in adults. Design Generalisation to the untrained ear and untrained 2000 Hz frequency was assessed following single-session or nine-session 1000 Hz FD training, using an adaptive forced-choice procedure. Two additional groups served as controls for the untrained frequency. Study sample Fifty-four children aged 7–9 years and 59 adults aged 18–30 years. Results (1) Only adult-like learners generalised their learning gains across frequency or ear, albeit less efficiently than adults; (2) As training duration increased children experienced reduced generalisation, similar to adults; (3) Children’s performance in the untrained tasks correlated strongly with their trained task performance after the first training session. Conclusions Auditory skill learning and its generalisation do not necessarily mature contemporaneously, although mature learning is a prerequisite for mature generalisation. Furthermore, in children, as in adults, more practice makes rather specific experts. These findings should be considered when designing training programs.

Single EX-CCCII-Based First-Order Versatile Active Filter

This paper presents a new current-mode first-order versatile active filter employing one extra-x second-generation current controlled current conveyor (EX-CCCII) and one grounded capacitor. The proposed filter can realize first-order filtering functions of a low-pass filter (LPF), high-pass filter (HPF), and all-pass filter (APF) within the same topology with low-input and high-output impedances required for current-mode circuits. This multiple-output EX-CCCII-based filter can provide six transfer functions as both non-inverting and inverting filtering functions of the LPF, HPF, and APF are obtained. The filter also offers electronic control of the pole frequency of all filtering. The proposed current-mode filter can be applied to work as a mixed-mode active filter, namely in the transadmittance-mode (TAM), transimpedance-mode (TIM), and voltage-mode (VM). Each operation mode can provide six transfer functions. The proposed filter was simulated and designed using SPICE and 0.18 µm CMOS technology. Experimental results using the commercially available integrated circuit AD844 were used to confirm the functionality of the new circuits.

Low cross-talk optical addressing of trapped-ion qubits using a novel integrated photonic chip

Individual optical addressing in chains of trapped atomic ions requires the generation of many small, closely spaced beams with low cross-talk. Furthermore, implementing parallel operations necessitates phase, frequency, and amplitude control of each individual beam. Here, we present a scalable method for achieving all of these capabilities using a high-performance integrated photonic chip coupled to a network of optical fibre components. The chip design results in very low cross-talk between neighbouring channels even at the micrometre-scale spacing by implementing a very high refractive index contrast between the channel core and cladding. Furthermore, the photonic chip manufacturing procedure is highly flexible, allowing for the creation of devices with an arbitrary number of channels as well as non-uniform channel spacing at the chip output. We present the system used to integrate the chip within our ion trap apparatus and characterise the performance of the full individual addressing setup using a single trapped ion as a light-field sensor. Our measurements showed intensity cross-talk below ~10–3 across the chip, with minimum observed cross-talk as low as ~10–5.

Optimal capacity configuration and operation strategy of typical industry load with energy storage in fast frequency regulation

As the potential and competent load-side resources for frequency response and control in modern power grids, typical industrial load can compensate for the deficiency of frequency response capability for new-type power systems. However, the operational flexibility is seriously enforced by the operation conditions uncertainties of industrial load. With “Online Calculation, and Real-time Matching” as the core, based on fuzzy mathematical theory, the coordinated operation strategy of typical industrial loads and energy storage systems (ESS) is proposed to finish fast frequency regulation (FFR) tasks. And an optimal capacity configuration model of industrial loads with ESSs is established to evaluate the whole economic profitability in FFR. Specially, in terms of algorithms, a novel Two-layer Parallel Particle Swarm Optimization and Genetic Algorithm (TPPSGA) is innovatively designed in this manuscript. Comparing with traditional heuristic algorithms, TPPSOGA improves computation speed by 6.84 times. Simulation results show that our proposed strategy can reasonably estimate the frequency capability of industrial load by membership degree function, and prove that the industrial load participating in FFR with the support of ESSs is able to get more revenue and stability than industrial load or ESSs separately is in regulation of FFR. The economic analyze in market price and ESS parameters can help the load agent to screen the suitable-type ESS and its capacity to serve for FFR and enhance the flexibility in load-side frequency regulation.

Innovative real-time deloading approach to wind systems with applications in primary frequency control

The integration of wind systems into the electrical grid faces a significant challenge due to the absence of inertia, a fundamental characteristic of this system. This lack of internal inertia directly impacts frequency stability. To address this challenge, various techniques have been developed, including storage systems, inertia emulation, and deloading methods, among others. Among these approaches, the latter method appears to be more suitable. This study introduces a novel deloading approach that offers several advantages. The proposed strategy is an online technique that is easy to implement. It does not necessitate mathematical models, estimations, prior knowledge of wind turbine (WT) characteristics, or the use of an extra sensor, thereby simplifying the approach. This approach is based on a WT cycle consisting of two consecutive operating modes: a maximum power measurement (MPM) mode and a power reserve (PR) mode. These modes are controlled by two controllers: a pitch angle controller (PAC) and a perturb and observe (P&O) algorithm controller. The cycle is managed and initiated using a monitoring signal with multiple configurations, providing various solutions and implementation possibilities. The effectiveness of this approach has been verified through numerous simulation tests conducted on both finite and isolated grids. These simulations employ theoretical and actual wind speed signals in the MATLAB Simulink environment, confirming the practicality and reliability of the proposed method.

Optimizing active distribution microgrids with multi-terminal soft open point and hybrid hydrogen storage systems for enhanced frequency stability

The recent increased interest in active distribution microgrids has complicated the process of reaching stable generation with the presence of distributed generators (DGs). These challenges can be handled by employing multi-terminal soft open points (SOPs), which can further boost system performance compared to the conventional two-terminal SOP, especially with present system disturbances. At the same time, hydrogen energy storage has drawn increased attraction to strengthen power grid stability and flexibility. This paper uses a hybrid-based energy storage device that employs an electrolyzer and fuel cell means with a hydrogen tank to absorb or generate power through multi-terminal SOP based on desired grid requirements. This work also offers a novel implementation of a hybrid jellyfish search and particle swarm optimization (HJPSO) applied to model predictive controllers (MPCs) to provide a solution regarding frequency control issues for multiple microgrids comprised of hybrid micro-turbines and renewable generators with the presence of storage devices. Furthermore, different forms of nonlinearity and actual data measurements for the implemented renewable generators are incorporated to attain further realistic analysis. The transient behavior of the studied microgrid is substantially augmented via the suggested control scheme. Referring to the modern grid codes and standards, the frequency operation limits in a specific range. This requirement has been preserved throughout the entire simulations, including uncertainty analysis, assessing the capabilities of multi-terminal SOP in providing frequency disturbance ride-through for the operating active hybrid distribution microgrid under various operating conditions.

Tube-based MPC strategy for load frequency control of multi-area interconnected power system with HESS

With the rapid development of power generation technology and the increasing demand from power users, multi-area interconnected power systems have become a development trend. This article proposes a new robust tube-based model predictive control strategy (MPC) for multi-area interconnected power systems with hybrid energy storage systems (HESS), based on the application of HESS to optimize the performance of load frequency control in power systems. The proposed strategy is mainly based on a new robustness constraint, which effectively handles uncertain disturbances within interconnected power system by compressing the disturbance invariant set. This strategy effectively alleviates the problems caused by inconsistency in multi-area interconnection modes. In addition, a sufficient stability criterion is derived to ensure the robust stability of the system, even under uncertain disturbances. By modeling a four-area interconnected power system with HESS, the frequency fluctuations of different methods are compared and discussed in each area. Based on the constructed software-in-the-loop setup, the effectiveness and feasibility of the proposed strategy are verified by experiments.

False Data Injection Attack Detection, Isolation, and Identification in Industrial Control Systems Based on Machine Learning: Application in Load Frequency Control

The integration of advanced information and communication technology in smart grids has exposed them to increased cyber attacks. Traditional model-based fault detection systems rely on mathematical models to identify malicious activities but struggle with the complexity of modern systems. This paper explores the application of artificial intelligence, specifically machine learning, to develop fault detection mechanisms that do not depend on these models. We focus on operational technology for fault detection, isolation, and identification (FDII) within smart grids, specifically examining a load frequency control (LFC) system. Our proposed approach uses sensor data to accurately identify threats, demonstrating promising results in simulated environments.

Compatibility Analysis of Frequency Containment Reserve and Load Frequency Control Functions

The study focuses on investigating the behavior of Electric Power Systems (EPS) during desynchronization from a synchronous area, its stability in island mode operation, and its synchronization with Continental Europe (CE). The primary aim is to ensure and maintain the stability and reliable operation of self–operating individual EPSs by analyzing the technical requirements and prequalification procedures for Frequency Containment Reserves (FCR) and Load Frequency Control (LFC) reserves. The study employed numerical simulations to analyze the behavior of isolated EPSs after losing a certain amount of active power, to ensure reliable operation and avoid the effects of Under Frequency Load Shedding (UFLS). It examined the technical requirements for LFC reserve units, such as the ability to remain connected to the network during disturbances and provide necessary support. The research investigated the procedures for prequalifying reserve units to provide FCR and LFC services. This investigation contributes to ensuring that EPSs operate reliably during desynchronization and that reserve providers meet the necessary technical and operational standards.

Laser harmonic generation with independent control of frequency and orbital angular momentum

The non-linear optical process of laser harmonic generation (HG) enables the creation of high quality pulses of UV or even X-ray radiation, which have many potential uses at the frontiers of experimental science, ranging from lensless microscopy to ultrafast metrology and chiral science. Although many of the promising applications are enabled by generating harmonic modes with orbital angular momentum (OAM), independent control of the harmonic frequency and OAM level remains elusive. Here we show, through a theoretical approach, validated with 3D simulations, how unique 2-D harmonic progressions can be obtained, with both frequency and OAM level tuned independently, from tailored structured targets in both reflective and transmissive configurations. Through preferential selection of a subset of harmonic modes with a specific OAM value, a controlled frequency comb of circularly polarised harmonics can be produced. Our approach to describe HG, which simplifies both the theoretical predictions and the analysis of the harmonic spectrum, is directly applicable across the full range of HG mechanisms and can be readily applied to investigations of OAM harmonics in other processes, such as OAM cascades in Raman amplification, or the analysis of harmonic progressions in nonlinear optics.

Experimental Investigation of a Distributed Architecture for EV Chargers Performing Frequency Control

The demand for electric vehicle supply equipment (EVSE) is increasing because of the rapid shift toward electric transport. Introducing EVSE on a large scale into the power grid can increase power demand volatility, negatively affecting frequency stability. A viable solution to this challenge is the development of smart charging technologies capable of performing frequency regulation. This paper presents an experimental proof of concept for a new frequency regulation method for EVSE utilizing a distributed control architecture. The architecture dynamically adjusts the contribution of electric vehicles (EVs) to frequency regulation response based on the charging urgency assigned by the EV users. The method is demonstrated with two Renault ZOEs responding to frequency fluctuation with a combined power range of 6 kW in the frequency range of 50.1 to 49.9 Hz. The results confirm consistent power sharing and effective frequency regulation, with the system controlling the engagement of the EVs in frequency regulation based on priority. The delay and accuracy analyses reveal a fast and accurate response, with the cross-correlation indicating an 8.48 s delay and an average undershoot of 0.17 kW. In the conclusions, the paper discusses prospective improvements and outlines future research directions for integrating EVs as service providers.

Frequency regulation controller for multi‐area interconnected power system

AbstractBy increasing the number of electric vehicles (EVs) to achieve a less carbon environment, not only they consume power from the grid to be charged, but also do they deliver power to the grid, and this can play a significant role in load frequency control. However, EVs take part in frequency regulation based on their state of charge (SoC) which will cause uncertainties. In order to manage these uncertainties, EV aggregator (EVA) concept has been introduced. The EV owner participates in the demand response program provided by the EVA arbitrarily considering her/his requirements. Accordingly, EVA calculates the up and down power reserve for the power system operator. Following any frequency deviation, EVA sends the proper commands to each EV to consume or to inject power to the system. There are also communication delays between different parts, uncertainties, and non‐linearities that existed in the power system. To overcome these issues, this study proposes a new robust load frequency controller based on feedback theory and artificial neural network which is designed through the non‐linear multi‐machine power system. Simulation results on IEEE 39‐bus power system show that the proposed controller regulates frequency more desirably in comparison with other methods.