A Dynamic Coherency Identification Method Based on Frequency Deviation Signals

Scatter contamination constitutes a dominant source of degradation of image quality in cone-beam computed tomography (CBCT). We have recently developed an analytic image reconstruction method with a scatter correction capability from the partially blocked cone-beam data out of a single scan. Despite its easy implementation and its computational efficiency, the developed method may result in additional image artifacts for a large cone angle geometry due to data inconsistency. To improve the image quality at a large cone angle, we propose a weighted rebinned backprojection-filtration (wrBPF) algorithm in conjunction with a hybrid type scatter correction approach. The proposed method uses a beam-blocker array that provides partial data for scatter correction and image reconstruction and that only blocks the beam within a limited cone angle. Thisdesign allows a chance to keep the image quality at larger cone angles by use of data redundancy since the projection data corresponding to larger cone angles are not blocked. However, the scatter correction would not be straightforward. In order to correct for the scatter in the projections at larger cone angles, we propose a novel scatter correction method combining a measurement-based and a convolution-based method. We first estimated the scatter signal using a measurement-based method in the partially beam-blocked regions, and then optimized the fitting parameters of a convolution-kernel that can be used for scatter correction in the projections at larger cone angles. For image reconstruction, we developed a wrBPF with butterfly filtering. We have conducted an experimental study to validate the proposed algorithm for image reconstruction and scatter correction. The experimental results revealed that the developed reconstruction method makes full use of the benefits of partial beam-blocking for scatter correction and image reconstruction and at the same time enhances image quality at larger cone angles by use of an optimized convolution-based scatter correction. The proposed method that enjoys the advantages of both measurement-based and convolution-based methods for scatter correction has successfully demonstrated its capability of reconstructing accurate images out of a single scan in circular CBCT.

The desire to reduce model-dependency has driven measurement-based modal identification methods, as an alternative to analyzing linearized system models. One such measurement-based method is Dynamic Mode Decomposition (DMD), which has recently appeared in literature. Using this relatively fast DMD algorithm, this paper presents an interactive modal-identification tool for spatio-temporal analysis of measurement data. The tool can automatically scan through measurements, and display the values of oscillation frequency and damping ratio, as well as reconstruct signals. The use of this tool, its options, and visualization capabilities are illustrated using simulated measurements from an interconnected power grid.

Problem statement. One of the disadvantages of radio engineering systems using the Doppler effect is the lack of devices for amplifying the frequency deviations of the received signal, which limits the resolution of systems for monitoring the parameters of moving objects. Therefore, the development of signal frequency deviation amplifiers by analogy with signal amplitude and power amplifiers is an urgent task today. Objective. To present the structure of a device for amplifying signal frequency deviations due to the Doppler effect, to substantiate the operating principle and to show the technology of its adjustment. Results. The structure of a harmonic signal frequency deviation amplifier is proposed. Computer models of a three-frequency generator in the mode of amplifying the frequency deviations of the input excitation signal and generating an output harmonic signal with an increased frequency deviation are developed. Requirements for the phase-frequency characteristics of the band-pass amplifiers of the three-frequency generator are determined. During dynamic modeling, the efficiency of using an amplifier of harmonic signal frequency deviations arising due to the Doppler effect is confirmed. Practical significance. The presented results allow us to develop recommendations for the practical implementation of a device for amplifying frequency deviations of a harmonic signal.

This paper presents a new coherency identification method for dynamic reduction of a power system. To achieve dynamic reduction, coherency-based equivalence techniques divide generators into groups according to coherency, and then aggregate them. In order to minimize the changes in the dynamic response of the reduced equivalent system, coherency identification of the generators should be clearly defined. The objective of the proposed coherency identification method is to determine the optimal coherent groups of generators with respect to the dynamic response, using the Partitioning Around Medoids (PAM) algorithm. For this purpose, the coherency between generators is first evaluated from the dynamic simulation time response, and in the proposed method this result is then used to define a dissimilarity index. Based on the PAM algorithm, the coherent generator groups are then determined so that the sum of the index in each group is minimized. This approach ensures that the dynamic characteristics of the original system are preserved, by providing the optimized coherency identification. To validate the effectiveness of the technique, simulated cases with an IEEE 39-bus test system are evaluated using PSS/E. The proposed method is compared with an existing coherency identification method, which uses the K-means algorithm, and is found to provide a better estimate of the original system.

Coherency detection in power system is vital step for controlled‐islanding. Model‐based slow‐coherency method is the traditional method of detecting coherent areas, which suffers from computational burdens. With the implementation of a wide‐area measurement system (WAMS) in power systems, coherent areas can be detected early after a disturbance that helps to ensure quick separation or remedial action. In this study, a novel Synchrophasor‐based coherency identification method has been proposed with bus voltage angle measurement. A new algorithm is proposed based on the first and second derivatives of the bus voltage phase angle using hierarchical clustering method for coherency detection. The proposed approach leverages hierarchical clustering method and the dynamics reflected in the bus voltage phase angle behavior to achieve automatic and accurate coherency identification. Real time simulation results demonstrate the ability of the proposed algorithm to detect the coherency with three consecutive PMU data from all the system buses and showcase coherent area boundaries. Case studies show robustness of the proposed method and is least affected by the latency in communication network compared to conventional methods. The proposed algorithm has been mathematically formulated and evaluated with different benchmark systems in OPAL‐RT real‐time simulation environment with HYPERSIM platform.

Applications of volumetric CT (VCT) are hampered by shading and streaking artifacts in the reconstructed images. These artifacts are mainly due to strong x-ray scatter signals accompanied with the large illumination area within one projection, which lead to CT number inaccuracy, image contrast loss and spatial nonuniformity. Although different scatter correction algorithms have been proposed in literature, a standard solution still remains unclear. Measurement-based methods use a beam blocker to acquire scatter samples. These techniques have unrivaled advantages over other existing algorithms in that they are simple and efficient, and achieve high scatter estimation accuracy without prior knowledge of the imaged object. Nevertheless, primary signal loss is inevitable in the scatter measurement, and multiple scans or moving the beam blocker during data acquisition are typically employed to compensate for the missing primary data. In this paper, we propose a new measurement-based scatter correction algorithm without primary compensation for full-fan VCT. An accurate reconstruction is obtained with one single-scan and a stationary x-ray beam blocker, two seemingly incompatible features which enable simple and efficient scatter correction without increase of scan time or patient dose. Based on the CT reconstruction theory, we distribute the blocked data over the projection area where primary signals are considered approximately redundant in a full scan, such that the CT image quality is not degraded even with primary loss. Scatter is then accurately estimated by interpolation and scatter-corrected CT images are obtained using an FDK-based reconstruction algorithm. The proposed method is evaluated using two phantom studies on a tabletop CBCT system. On the Catphan©600 phantom, our approach reduces the reconstruction error from 207 Hounsfield unit (HU) to 9 HU in the selected region of interest, and improves the image contrast by a factor of 2.0 in the high-contrast regions. On an anthropomorphic head phantom, the reconstruction error is reduced from 97 HU to 6 HU in the soft tissue region and image spatial nonuniformity decreases from 27% to 5% after correction. Our method inherits the main advantages of measurement-based methods while avoiding their shortcomings. It has the potential to become a practical scatter correction solution widely implementable on different VCT systems.

Research on improved dynamic load identification method based on Kalman filter under noise interference

Deep regression adaptation networks with model-based transfer learning for dynamic load identification in the frequency domain

Currently, four methods are mainly used to evaluate the parameters of an asynchronous motor with a short-circuited rotor(AD with a short-circuited rotor): according to catalog data, according to test results, preliminary parameter estimation and dynamic identification. Each of the above methods has its advantages and disadvantages, however, the only method that allows you to evaluate the parameters of the AD model with a short-circuited rotor without stopping the process is the method of dynamic parameter identification. The advantages of the method of dynamic identification of parameters of the asynchronous motor replacement circuit (AD), in addition to the absence of the need to stop the technological process, include the ability to evaluate the parameters of the AD model taking into account its dynamically changing temperature state. In turn, the disadvantage of this method of parameter estimation is the need to build a complex identification procedure, which requires taking into account many factors and does not have a generally recognized mathematical description. In connection with the identified problems, it can be argued that expanding the arsenal of tools that allow determining blood pressure parameters without stopping the technological process is an urgent and timely task

Multicast admission control in differentiated services network is an important but shortly researched subject. We propose a parameter-based admission control method. The method rejects new multicast join requests that would otherwise decrease the quality experienced by the existing receivers. DiffServ network edge nodes filter join requests and generate new requests. The proposed method is developed as an extension to the DSMCast protocol but could also be adapted to other protocols. In this paper the parameter-based admission control is compared to earlier created, measurement-based admission control methods, as well to situation when no admission control is used.

Hybrid power generation system such as photovoltaic (PV)-diesel is one of the solution to reduce operational cost of the conventional diesel generation system. However, fluctuation of PV power due to uncertainty in weather condition may generate an unstable frequency of system. Further, it may reduce the reliability of the power utility, especially when the penetration of PV power is large. Therefore, a frequency control approach that involving PV with battery supervised by neural network algorithm is proposed to generate a stable system frequency. This method can reduce the frequency deviation without smoothing PV output power. The PV panels can generate maximum power according to the weather condition and frequency deviation. The PV output power and battery output power is controlled by neural network algorithm control. The neural network algorithm control is considering frequency deviation, average insolation and change of insolation as input. The proposed method is compared with similar system but without the existence of battery as energy storage system. The simulation results show that the proposed method can generate small frequency deviation compared with system without battery.

A fuzzy based method for leveling output power fluctuations of photovoltaic-diesel hybrid power system

The determination of structural dynamic characteristics can be challenging, especially for complex cases. This can be a major impediment for dynamic load identification in many engineering applications. Hence, avoiding the need to find numerous solutions for structural dynamic characteristics can significantly simplify dynamic load identification. To achieve this, we rely on machine learning. The recent developments in machine learning have fundamentally changed the way we approach problems in numerous fields. Machine learning models can be more easily established to solve inverse problems compared to standard approaches. Here, we propose a novel method for dynamic load identification, exploiting deep learning. The proposed algorithm is a time-domain solution for beam structures based on the recurrent neural network theory and the long short-term memory. A deep learning model, which contains one bidirectional long short-term memory layer, one long short-term memory layer and two full connection layers, is constructed to identify the typical dynamic loads of a simply supported beam. The dynamic inverse model based on the proposed algorithm is then used to identify a sinusoidal, an impulsive and a random excitation. The accuracy, the robustness and the adaptability of the model are analyzed. Moreover, the effects of different architectures and hyperparameters on the identification results are evaluated. We show that the model can identify multi-points excitations well. Ultimately, the impact of the number and the position of the measuring points is discussed, and it is confirmed that the identification errors are not sensitive to the layout of the measuring points. All the presented results indicate the advantages of the proposed method, which can be beneficial for many applications.

The integration of diverse energy sources and the advent of smart grids have intensified the challenges in load frequency management (LFM). Modern power systems are increasingly vulnerable to inherent nonlinearities, such as generation rate constraints, governor dead bands, boiler dynamics, and communication delays, as well as sophisticated cyber-attacks, which collectively threaten frequency stability and tie-line power balance. To address these challenges, this study proposes a novel cascade controller, designated as (1 + FOPI)-FOPI-TID, for robust automatic generation control in hybrid two-area power systems. The controller uniquely combines fractional-order (FO) dynamics with a tilt-integral-derivative stage and is optimized using a green metaheuristic, the weighted average algorithm (WAA). The WAA effectively balances exploration and exploitation to achieve superior parameter tuning. The proposed control architecture processes both area control error (ACE) and frequency deviation (ΔF) signals through dedicated stages, enabling enhanced disturbance rejection and transient response. The system model incorporates a comprehensive set of nonlinearities and evaluates resilience against resonance-based cyber-attacks. Comprehensive simulation studies under both AC and HVDC tie-line configurations demonstrate that the WAA-optimized (1 + FOPI)-FOPI-TID controller significantly outperforms existing schemes, including PD-PI, PIFOD-(1 + PI), and PIDF(1 + FOD). Key performance metrics show a 45.3% reduction in the integral of time-weighted absolute error (ITAE) and improvements in settling times of 47.7% for ΔF₁ and 32.8% for ΔF₂. Sensitivity analysis confirms robustness under ± 25% parameter variations and random load perturbations. During cyber-attacks, the controller maintains the lowest Rate of Change of Frequency (RoCoF), underscoring its dual capability in stabilizing grid dynamics and mitigating cyber-physical threats. These results validate the controller's potential to enhance operational resilience and reliability in future smart grids.

A photovoltaic (PV) system's power output is not constant and fluctuates depending on weather conditions. Fluctuating power causes frequency deviations and reduction in reliability of the isolated power utility or microgrid when large output power from several PV systems is penetrated in the utility. In this paper, to overcome these problems, a simple coordinated control method for leveling the fluctuations of combined power output from multiple PV systems is proposed. The conflicting objective of output power leveling and acquisition power increase is achieved by means of the proposed method. Here, output power command is generated in two steps: central and local. Fuzzy reasoning is used to generate the central leveling output power command considering insolation, variance of insolation, and absolute average of frequency deviation. In local step, a simple coordination is maintained between central power command and local power commands by producing a common tuning factor. Power converters are used to achieve the same output power as local command power employing PI control law for each of the PV generation systems. The proposed method is compared with the method where a modified maximum power point tracking control is used for smoothing the short-term change in each of the PV system's output. Simulation results show that the proposed method is effective for leveling output power fluctuations and feasible to reduce the frequency deviations of the isolated power utility to maintain reliability.