Power Deviation Research Articles

A Novel Design of an Oral Appliance for Monitoring Electromyograms of the Genioglossus Muscle in Obstructive Sleep Apnea Syndrome.

Obstructive sleep apnea (OSA) is a prevalent source of sleep-disordered breathing. OSA is most commonly associated with dysfunctions in the genioglossus (GG) muscle. In this study, we present the first version of a medical device that produces an electromyogram (EMG) of the GG. The prototype is composed of a (custom-made) 3D-printed mouthpiece. Impressions were taken for the lower arch and scanned with a lab scanner to be converted into digital impressions. ExoCad software was used to design the appliance. Fusion 360 software was then used to modify the design and create tubes to house the electrodes in a bilateral configuration to secure excellent and continuous contact with the GG muscle. Silver-silver chloride electrodes were incorporated within the appliance through the created tubes to produce a muscle EMG. In this preliminary prototype, an EMG amplifier was placed outside the mouth, and isolated electric wires were connected to the amplifier input. To test the design, we ran experiments to acquire EMG signals from a group of OSA patients and a control group in wakefulness. The GG EMGs were acquired from the participants for 60 s in a resting state whereby they rested their tongues without performing any movement. Then, the subjects pushed their tongues against the fontal teeth with steady force while keeping the mouth closed (active state). Several features were extracted from the acquired EMGs, and statistical tests were applied to evaluate the significant differences in these features between the two groups. The results showed that the mean power and standard deviation were higher in the control group than in the OSA group (p < 0.01). Regarding the wavelength during the active state, the control group had a significantly longer wavelength than the OSA group (p < 0.01). Meanwhile, the mean frequency was higher in the OSA group (p < 0.01) at rest. These findings support research that showed that impairment in GG activity continues in the daytime and does not only occur during sleep. Future research should focus on developing the device to be more user-friendly and easily used at home during wakefulness and sleep.

Power systems stabilizers online tuning based upon parameters dynamic estimation

This paper deals with the tuning of power system stabilizers based upon an estimation procedure properly tailored for online applications. The growth of renewables in modern power systems imposes constant verification of the current operating condition and the effective tuning of power system stabilizer parameters to comply with a satisfactory damping factor of general system behavior. More specifically, an online procedure is proposed for the estimation of the current operating point and the consequent tuning of the parameters of the power system stabilizers. The estimation procedure combines the system’s dynamic response to a probing signal with the available measurements of power and voltage at the generator terminal side. The parameters of the current operating point are then derived through the least squares minimization between the measurement and power deviations. At the aim of the parameter tuning procedure, with respect to the estimated operating point, proper modeling that considers the presence of damper windings within the design methodology is proposed for better describing the dynamic performances. The proposed design procedure can combine both the specifications in the frequency domain of the phase compensation and those relating to the appropriate location of the eigenvalues of the dynamic matrix of the system. In the numerical application, the proposed approach is shown to be very effective in terms of current operating point estimation and consequent tuning of the power system stabilizer parameters in the range of interarea oscillations and local oscillations, thus counteracting the renewed requirements for stability issues in modern power systems.

Optimal Power Model Predictive Control for Electrochemical Energy Storage Power Station

Aiming at the current power control problems of grid-side electrochemical energy storage power station in multiple scenarios, this paper proposes an optimal power model prediction control (MPC) strategy for electrochemical energy storage power station. This method is based on the power conversion system (PCS) grid-connected voltage and current to establish a power prediction model for energy storage power stations, achieving a one-step prediction of the power of the power station. The power prediction error is used as a power regulation feedback quantity to correct the reference power input. Considering the state of charge (SOC) constraint of the battery, partition the SOC into different states. Using SOC as the power regulation feedback, the power of the battery compartment can be adjusted according to the range of the battery SOC to prevent SOC from exceeding the limit value, simultaneously calculating the power loss of the energy storage power station to improve the energy efficiency. The objective function is to minimize the power deviation and power loss of the power station. By solving the objective function, the optimal switching voltage vector of the converter output is achieved to achieve optimal power control of the energy storage power station. The simulation results in various application scenarios of the energy storage power station show that the proposed control strategy enables the power of the storage station to quickly and accurately track the demand of grid scheduling, achieving the optimal power control of the electrochemical energy storage power station.

On the optimal multi-objective design of fractional order PID controller with antlion optimization

This study introduces an optimal multi-objective design approach for a robust multimachine Fractional Order PID Controller (FOPID) using the Antlion algorithm. The research focuses on the need for effective stabilizers in multimachine power systems by employing traditional speed-based lead-lag FOPID controllers. The study formulates a multi-objective problem, optimizing the damping factor and damping ratio of lightly damped electromechanical modes to maximize a composite set of objective functions, tackled through the Antlion algorithm. Stability analysis of Single-Machine Infinite-Bus (SMIB) and multimachine power systems is conducted based on rotor speed and power deviation minimization in the time domain response, along with damping ratio and eigenvalue analysis. The proposed approach is implemented and tested on three IEEE test cases, showcasing significant improvements in stability through the reduction of maximum overshoot (Mp) and settling time (ts) of speed deviation. Comparative analysis with other optimization-based FOPID controllers underscores the superiority of the proposed approach in enhancing stability in multimachine power systems. The main impact of this research lies in its contribution to the advancement of stability enhancement techniques in multimachine power systems, offering a systematic framework for optimal FOPID controller design and empowering decision-making processes in power engineering.

Deviation control strategy for electric vehicle virtual energy storage dispatching instruction execution

Abstract As flexible resources, electric vehicles (EVs) participate in the dispatching of power grid regulation. Due to the randomness of entering and exiting the station, each EV cluster cannot fully respond to the guiding power instructions issued by the operator, resulting in biased values. This paper establishes a deviation management and control model, and proposes a local deviation control strategy and a cross-cluster collaborative deviation control strategy based on the model, so as to fully utilize the deviation control capabilities of each EV’s virtual energy storage and minimize the power deviation. The effectiveness of the proposed deviation control strategy for scheduling instructions is verified by numerical examples.

A Wind Power Fluctuation Smoothing Control Strategy for Energy Storage Systems Considering the State of Charge

With the significant increase in the scale of energy storage configuration in wind farms, improving the smoothing capability and utilization of energy storage has become a key focus. Therefore, a wind power fluctuation smoothing control strategy is proposed for battery energy storage systems (BESSs), considering the state of charge (SOC). First, a BESS smoothing wind power fluctuation system model based on model predictive control (MPC) is constructed. The objective function aims to minimize the deviation of grid-connected power from the target power and the deviation of the BESS’s remaining capacity from the ideal value by comprehensively considering the smoothing effect and the SOC. Second, when the wind power’s grid-connected power exceeds the allowable fluctuation value, the weight coefficients in the objective function are adjusted in real time using the first layer of fuzzy control rules combined with SOC partitioning. This approach smooths wind power fluctuations while preventing overcharging and overdischarging of the BESS. When the grid-connected power is within the allowable fluctuation range, the charging and discharging power of the BESS is further refined using a second layer of fuzzy control rules. This enhances the BESS’s capability and utilization for smoothing future wind power fluctuations by preemptively charging and discharging. Finally, the proposed control strategy is simulated using MATLAB R2021b with actual operational data from a wind farm as a case study. Compared to the traditional MPC control method, the simulation results demonstrate that the proposed method effectively controls the SOC within a reasonable range, prevents the SOC from entering the dead zone, and enhances the BESS’s ability to smooth wind power fluctuations.

Investigation of reciprocal rank method for automatic generation control in two-area interconnected power system

In this article, the design and performance analysis of Jaya-assisted reciprocal rank-based proportional–integral–derivative controller with derivative filter (PIDn) is proposed for the automatic generation control (AGC) problem of two-area interconnected power systems. The filter with a derivative gain is used to nullify the impact of noise in the input signal. The integral of time multiplied square error (ITSE) of frequency deviations, tie-line power deviations, and area-control errors is the foundation of the goal function developed for tuning controller parameters (ACEs). A single overall objective function is created by combining these sub-objectives. ITSEs of two areas, ITSEs of tie-line power deviation, and ITSEs of ACEs of two areas are weighted and summarized to form the overall goal function. Each sub-relative goal’s importance in the control design is evaluated using the weights in the overall objective function. The weights in this article are determined using the reciprocal rank approach in a methodical manner, in contrast to earlier techniques where weights were either assumed equally by ignoring the relative relevance of sub-objectives or chosen at random. The resultant total objective function is minimized using the Jaya method. Six different test cases involving various load disturbances in the interconnected areas are used to evaluate the performance of the suggested Jaya optimizer-assisted reciprocal rank technique-based controller. Additionally, the performance of various controllers tuned with the sine cosine algorithm (SCA), salp swarm algorithm (SSA), symbiotic organisms search (SOS), Nelder–Mead simplex (NMS), and Luus–Jaakkola (LJ) algorithms is compared with that of the Jaya-tuned controller. The time domain specifications are provided for each of the six test scenarios. To display the differences in frequencies and tie-line power, the results are also shown. Statistical analysis is also provided to assess the suggested controller’s overall efficacy.

LES-based validation of a dynamic wind farm flow model under unsteady inflow and yaw misalignment

This work presents the validation of an extended version of the control-oriented, dynamic wind farm flow solver SPLINTER. The two-dimensional model is applied to use cases of wake steering by yaw misalignment and inflow wind direction variations and the results are compared to large-eddy simulations (LES). While SPLINTER is able to reproduce the antagonal behaviour of decreasing upstream and increasing downstream turbine power under wake deflection, a systematic deviation of the downstream power is detected and quantified, which is connected to underrepresented three-dimensional wake effects. In case of changing inflow wind direction, SPLINTER is capable of computing movement and shape of the bending wakes. The model smooths small-scale turbulent structures and disturbances and does not reproduce wake meandering, but manages to describe the evolution of the mean flow, which is tested by averaging over an ensemble of LES and comparing the resulting flow fields and turbine power time series. Under dynamic inflow conditions, SPLINTER is able to predict at which time intervals and at which rates downstream turbines will be influenced by wakes, which can improve the accuracy of short-term power and load forecasting and enables its application to online model predictive wind farm control.

Profit-optimal data-driven operation of a hybrid power plant participating in energy markets

An energy management system (EMS) is formulated for a hybrid power plant (HPP), consisting of a wind power plant and battery storage plant, participating in bidding stages in the German energy market. The EMS utilizes supervisory control and data acquisition (SCADA) measurements from the site to improve power forecast from the wind power plant. First, the measurement data are used together with numerical weather prediction data to accurately forecast local wind conditions. Second, the measurement data are used to adapt a baseline engineering wake model that gives the total wind power generation for a given input wind condition. The EMS also uses an online cyclic damage minimization approach to accurately balance the battery damage cost against the revenue obtained by market bidding. An HPP controller is formulated to ensure proper tracking of optimal set-points. When compared with standard formulations, the proposed approach shows an accurate estimation and balancing of revenue and costs and a significant reduction in the power deviation penalty, which leads to significantly higher overall profit.

Frequency Regulation Adaptive Control Strategy of Wind Energy Storage System for Wind Speed Uncertainty

In order to reduce the negative influence of wind speed randomness and prediction error on frequency modulation, the reliability of the wind storage system was assessed effectively. In the wind storage frequency modulation system, a state of charge (SOC) adaptive adjustment method for wind speed randomness is proposed. Firstly, through the correlation analysis between the standby capacity of frequency modulation and the output power of wind turbine, the uncertainty of its frequency modulation capacity is revealed. Secondly, in view of the uncertainty of wind turbine frequency modulation, the output power of energy storage frequency modulation is optimized with the goal of minimizing the frequency modulation power deviation of the wind storage front under the framework of model predictive control, and the improved whale optimization algorithm (WOA) is used to solve the problem. Finally, the simulation results show that, under the given 5 min continuous disturbance, the root mean square of frequency regulation of the proposed restoration method is reduced by 56.65% compared to the SOC recovery base point set to 0.5. Under continuous large perturbations, the maximum frequency deviation is reduced by 0.0455 Hz. This effectively shows that this method can not only improve the frequency modulation reliability of wind power system but also improve the continuous frequency modulation capability of energy storage system.

Quantifying the performance and compensation of secondary frequency regulation of pumped storage plants considering variable-speed operation

Aiming at the “net-zero carbon” target, a higher proportion of variable renewable energies (VREs) has been integrated into power grids, and pumped storage plants (PSPs) are crucial for guaranteeing the safe and stable operation of hybrid energy systems. As secondary frequency regulation (SFR) is related to the economic operation and the quality of auxiliary services provided by PSPs, it is critical to clarify its performance and compensation. Therefore, the corresponding quantitative evaluations of the SFR of pumped storage units (PSUs) are carried out in this paper. First, the performance of SFR is quantified based on the fuzzy analytic hierarchy process (FAHP). Considering the current situation in China’s electricity market, the average compensation of the PSP with each MW is determined to be 9.336 CNY per day, and a method for calculating the compensation of SFR is constructed. Then, taking a real Chinese PSP as an example, based on different wind power deviation signals, the quantitative evaluation of the compensation of SFR is conducted in accordance with the fixed-speed pumped storage units (FSPSUs) and the variable-speed pumped storage units (VSPSUs) in power priority and speed priority modes. The results show that the compensation of FSPSUs and VSPSUs (power priority mode and speed priority mode) is 10,900 CNY per day; 54,400 CNY per day; and 17,300 CNY per day. This paper could contribute to clarifying the compensation of SFR and providing technical support for the development of the auxiliary service market.

An I–V characteristic reconstruction-based partial shading diagnosis and quantitative evaluation for photovoltaic strings

The partial shading condition (PSC) is the most common abnormality occurred in photovoltaic (PV) systems. Accurate quantitative evaluation of the shaded area and the severity of the shading is of potential importance in optimizing the maintenance strategy for PV systems. In this paper, we propose a PSC diagnosis and quantitative evaluation method by analyzing the measured string current–voltage (I–V) characteristic obtained from the PV inverter with the I–V scanning function, which includes pre-diagnosis of the system abnormality based on the operational power deviation, the diagnosis of PSCs based on the derivatives characteristics of the PV string, and the quantitative evaluation based on the I–V characteristic reconstruction. The quantitative evaluation of PSCs is the main focus in this paper, where the I–V characteristics of the unshaded PV modules in the partially shaded PV string are reconstructed according to different mismatch levels, respectively. The number of shaded PV modules and the corresponding severity of the partial shadings are estimated according to the reconstructed I–V characteristics. The simulation and experimental results verify that both the proposed diagnosis and quantitative evaluation method is effective with decent accuracy, especially for severe mismatch conditions. Experimental results show that the maximal mean absolute error of the quantified shaded area and quantified shaded rate are approximately 1.4446 units of 1/3 PV modules and 0.026, respectively.

A comprehensive simulation study of multi-junction solar cell

This study conducts comprehensive simulation analysis of typical triple-junction solar cells using Silvaco ATLAS. Initially, modeling and simulation of the typical triple-junction solar cells under the AM1.5 solar spectrum at 300 K are performed to characterize various performance parameters of the cells under one sun illumination. Subsequently, the impact of the thickness of the top and middle cell layers on the efficiency of the cells is analyzed. Additionally, the influence of doping concentration errors in individual sub-cells on the overall power deviation of the cells is investigated. Moreover, Al0.9Ga0.1As is employed to replace AlInP as the top FSF (Front Surface Field). Furthermore, the study analyzes the variation of internal radiation efficiency of the top and middle sub-cells of the solar cells with concentration factor and bias voltage. Finally, an assessment is made on the effects of different positions and densities of interface defects, as well as the introduction of class-acceptor Gaussian distribution state levels induced by defects, on the performance of the solar cells. The results indicate that this study provides valuable insights for optimizing multi-junction solar cells and analyzing internal physical phenomena.

VINUS: A neutron transport solver based on the variational nodal method for reactor core analysis

Compared to traditional transverse integration methods, the variational nodal method, with its unique advantages, is more suitable for high-fidelity calculations of reactor physics in reactors with complex geometries and finer detail descriptions. In this study, the basic theory of the variational nodal method was derived and the VINUS code is developed. The neutron solver based on this method is adaptable to various geometric models, and showcased the code's fundamental framework. On this basis, a set of self-designed macroscopic cross-section benchmarks, actual macroscopic cross-section benchmark VVER-440, and few-group microscopic cross-section benchmark RBEC-M for fast spectrum reactors were used to verify different functionalities of VINUS. The results were shown that VINUS code maintains good computational accuracy and convergence trends. For the VVER-440 benchmarks, the deviation of keff of VINUS from reference is less than 100 pcm, and the maximum power deviation is less than 4 %. For the RBEC-M, the deviation of keff is 125 pcm, and the maximum power deviation is less than 5 %. These outcomes collectively demonstrate the solver's potential for engineering applications in future advanced reactor designs.

DAR-LFC: A data-driven attack recovery mechanism for Load Frequency Control

In power systems, generation must be maintained in constant equilibrium with consumption. A key indicator for this balance is the frequency of the power grid. The load frequency control (LFC) system is responsible for maintaining the frequency close to its nominal value and the power deviation of tie-lines at their scheduled levels. However, the remote communication system of LFC exposes it to several cyber threats. A successful cyberattack against LFC attempts to affect the field measurements that are transferred though its remote control loop. In this work, a data-driven, attack recovery method is proposed against denial of service and false data injection attacks, called DAR-LFC. For this purpose, a deep neural network is developed that generates estimations of the area control error (ACE) signal. When a cyberattack against the LFC occurs, the proposed estimator can temporarily compute and replace the affected ACE, mitigating the effects of the cyberattacks. The effectiveness and the scalability of the DAR-LFC is verified on a single and a two area LFC simulations in MATLAB/Simulink.

Optimization method for freeform progressive addition lenses using the coincident degree of weight distributions for power deviation and astigmatism

This study proposed an optimization method for freeform progressive addition lenses (PALs) based on the coincident degree of weight distributions (WD) for power deviation and astigmatism. Compared with the existing methods which were limited in optimizing WDs for power deviation and astigmatism, our proposed approach offers a more refined optimization. In the design phase, the power deviation and astigmatism of these lenses were evaluated using the existing surface shape. Compared with the prescriptions of the patients, the coincident degrees between the obtained distributions and prescriptions of the PAL power and astigmatism were calculated in the multi-view axis condition. Normalization processing of coincident degrees was performed, yielding the corresponding threshold value of WDs and optimizing the allocated coincident degrees. Based on a minimization error function model, two PALs were designed, simulated, machined, and evaluated using a commercial software. The optimized method reduced peripheral astigmatism and improved the optical properties of PALs. The proposed approach optimizes the freeform PALs and enhances their design optimization in optometry.

Charging station service scope division and real-time pricing strategy based on the strongest occupation method

With the rapid construction of charging stations (CSs), charging station operators need to enhance their core competitiveness by precisely planning their service areas and formulating reasonable and effective pricing strategies. However, the regional competition among multiple charging station operators is generally ignored. In the traditional model, the service scope of CSs appears as regular circles, which is inconsistent with the market distribution law. In response to the irregular relationship between the dynamic service scope of CSs and the real-time charging price, a charging station service scope (CSSS) model is proposed by introducing the variable service field strength (SFS). First, the competitiveness of CSs is evaluated quantitatively, and the SFS of CSs is defined to describe the service scope of CSs by the strongest occupation method. Second, the impact of the charging price on the charging demand is analyzed based on the CSSS division model. In addition, the revenue of charging station operators and the stability of the power grid are considered to establish a real-time pricing optimization model. Finally, the numerical simulation is operated in Furong District of Changsha. It is shown that the proposed method effectively achieves more profits for charging stations and decreases the average power deviation of the whole region.

Performance Analysis of Automatic Generation Control for a Multi-Area Interconnected System Using Genetic Algorithm and Particle Swarm Optimization Technique

The primary focus of this paper is to assess an interconnected power system using different optimization techniques. The main purpose is to employ different optimization techniques, including genetic algorithms (GA) and particle swarm optimization (PSO), to systematically enhance the performance of a multi-area or two-area automatic generation control (AGC) system, aiming to optimize the three PID controllers gain values and improve system performance under diverse loading conditions. Two case studies are conducted exploring different loading conditions in the megawatt (MW) range, including increasing load demand and decreasing load demand. The analysis involves four scenarios, covering without any kind of controller, another with solely a proportional integral derivative (PID) controller, a PID controller enhanced through a genetic algorithm (GA), and lastly, a PID controller improved through particle swarm optimization (PSO). The optimization process utilizes the integral time absolute error (ITAE) as the objective function to evaluate the system's performance. The simulation outcomes for ITAE, settling time, overshoot, and undershoot for frequency deviation of area one, area two, and power deviation in the tie-line are compared with previous similar studies to assess the novelty of this work. The article highlights the importance of the multi-area AGC system and the significance of different optimization techniques in improving its performance.

23 KHz linewidth 1064 nm SOA based fiber laser by using parallel type subring cavities

This study presents a high-quality fiber ring laser created by integrating a semiconductor optical amplifier to generate amplified spontaneous emission with a four-subring resonator and utilizing the nonlinear polarization rotator effect. When the driven current of 400 mA, the laser exhibited a maximum power deviation of 0.204 dB and a wavelength deviation of 0.012 nm during a one-hour testing period. Furthermore, utilizing a delayed self-heterodyne measurement system, we measured the linewidth of the self-made fiber laser to be 23 kHz.

Cooperative game-based energy storage planning for wind power cluster aggregation station

It is possible to cut down the investment costs in energy storage and enhance the utilization of energy storage by planning the shared energy storage in the wind farm collection station to replace the dispersed energy storage of each wind farm. Considering the cluster complementary effects of multiple wind farms, this article proposes a cooperative game-based plan for the hybrid energy storage of battery and supercapacitor in the wind power cluster. Firstly, charging and discharging strategy for batteries follows the peak shaving and valley filling approach, while the strategy for supercapacitors aims to flatten power fluctuations and track power generation plans. Then, a dual-layer planning model for the shared energy storage station is established, and evaluation indicators for the energy storage configuration results are constructed. Finally, based on the improved Shapley value method, the profits of each wind farm are allocated, and the impact of energy storage investment costs on the results is analyzed. The case study results demonstrate that under this cooperative scheme, not only can the real-time output deviation of wind power be reduced, but also the investment costs of wind farms in energy storage systems can be reduced.