Oscillator Power Research Articles

A Novel Reinforcement Learning Algorithm-Based Control Strategy for Grid-Configured Inverters

To address the power oscillation problem due to the introduction of inertia and damping, this paper proposes a new deep reinforcement learning algorithm based on the SD3 (Softmax Deep Double Deterministic policy gradients) algorithm for grid configuration inverter control strategy to compensate for the loss of inertia and damping in the grid. Virtual synchronous generator control, as a typical grid configuration technique, inevitably brings stability problems while providing inertia and damping support to the grid. In this paper, we first analyze the nonlinear relationship between inertia and angular velocity, and find the key parameters to maintain the power stability; then, we migrate the deep reinforcement learning strategy, and design the control strategy applicable to the virtual synchronous generator; finally, through the adaption of the key parameters, we combine the control strategy with the grid-connected inverter, and solve the problem of the excessive grid-connected power oscillation of the inverter. The effectiveness and accuracy of the control method compared with other algorithms are verified by building a simulation model in MATLAB/Simulink, which realizes the purpose of reducing power oscillation.

A high-speed MPPT based horse herd optimization algorithm with dynamic linear active disturbance rejection control for PV battery charging system

This study first proposes an innovative method for optimizing the maximum power extraction from photovoltaic (PV) systems during dynamic and static environmental conditions (DSEC) by applying the horse herd optimization algorithm (HHOA). The HHOA is a bio-inspired technique that mimics the motion cycles of an entire herd of horses. Next, the linear active disturbance rejection control (LADRC) was applied to monitor the HHOA’s reference voltage output. The LADRC, known for managing uncertainties and disturbances, improves the anti-interference capacity of the maximum power point tracking (MPPT) technique and speeds up the system’s response rate. Then, in comparison to the traditional method (perturb & observe; P&O) and metaheuristic algorithms (conventional particle swarm optimization; CPSO, grasshopper optimization; GHO, and deterministic PSO; DPSO) through DSEC, the simulations results demonstrate that the combination HHOA-LADRC can successfully track the global maximum peak (GMP) with less fluctuations and a quicker convergence time. Finally, the experimental investigation of the proposed HHOA-LADRC was accomplished with the NI PXIE-1071 Hardware-In-Loop (HIL) prototype. The output findings show that the effectiveness of the provided HHOA-LADRC may approach a value higher than 99%, showed a quicker rate of converging and less oscillations in power through the detection mechanism.

Effects of centrally acting analgesics on resting-state electroencephalography biomarker candidates of chronic pain.

Resting-state electroencephalography (rsEEG) holds promise as a biomarker of chronic pain. However, the impact of centrally acting analgesics like opioids, antiepileptics, and antidepressants on rsEEG remains unclear. This confounds and limits the interpretability of previous studies and questions the specificity of rsEEG biomarker candidates of chronic pain, especially for potential diagnostic biomarkers. We, therefore, aimed to elucidate the effects of opioids, antiepileptics, and antidepressants on common rsEEG biomarker candidates of chronic pain. To this end, we analyzed two large, independent rsEEG datasets, including 217 people with chronic pain. We performed preregistered multivariate Bayesian analyses to allow for the quantification and interpretation of evidence in favor of as well as against medication effects on EEG. We specifically evaluated the effects of different centrally acting analgesics on rsEEG features and controlled for the potential confounds of age, pain intensity, and depression. Results predominantly provided evidence against effects of centrally acting analgesics on peak alpha frequency, oscillatory power in different frequency bands, and connectivity-based network measures. Although these findings do not rule out any effects of analgesics on rsEEG, they argue against medium to large effects of centrally acting analgesics on rsEEG. These results help to interpret previous and future rsEEG findings in people with chronic pain and strengthen the validity of rsEEG biomarker candidates of chronic pain. Thus, the present findings can help to develop clinically valuable biomarkers of chronic pain. PERSPECTIVE: This study investigated the effects of centrally acting analgesics on brain-based biomarker candidates of chronic pain, as assessed by electroencephalography. The results predominantly provided evidence against effects on peak alpha frequency, oscillatory power, and connectivity-based network measures. This might help to develop clinically useful biomarkers of chronic pain. DATA AVAILABILITY: Datasets are available at https://osf.io/uzgxw/files/osfstorage.

Spontaneous slow cortical potentials and brain oscillations independently influence conscious visual perception.

Perceptual awareness results from an intricate interaction between external sensory input and the brain's spontaneous activity. Pre-stimulus ongoing activity influencing conscious perception includes both brain oscillations in the alpha (7 to 14 Hz) and beta (14 to 30 Hz) frequency ranges and aperiodic activity in the slow cortical potential (SCP, <5 Hz) range. However, whether brain oscillations and SCPs independently influence conscious perception or do so through shared mechanisms remains unknown. Here, we addressed this question in 2 independent magnetoencephalography (MEG) data sets involving near-threshold visual perception tasks in humans using low-level (Gabor patches) and high-level (objects, faces, houses, animals) stimuli, respectively. We found that oscillatory power and large-scale SCP activity influence conscious perception through independent mechanisms that do not have shared variance. In addition, through mediation analysis, we show that pre-stimulus oscillatory power and SCP activity have different relations to pupil size-an index of arousal-in their influences on conscious perception. Together, these findings suggest that oscillatory power and SCPs independently contribute to perceptual awareness, with distinct relations to pupil-linked arousal.

Dynamic inverter station current control of HVDC system integrated offshore DFIG wind farm under onshore grid voltage distortions

A power control method for an HVDC system integrating a DFIG-based offshore wind farm supplying a weak onshore grid is the prime focus of this work. The real power generated by the wind farm is regulated by the LCC-based rectifier station and transmitted to the VSC-based inverter station through the HVDC line. The work proposes a control mechanism to integrate these two stations with better power quality management. The inverter station facilitates the effective transmission of generated power to the onshore grid under nominal onshore grid voltages. Nevertheless, the abnormalities in onshore grid voltage lead to converter failures due to huge harmonic grid currents and power pulsations beyond permissible limits; uninterrupted power transfer without compromising power quality is the target. The proposed method utilises a dynamic current control technique at the inverter station to inject compensating current. This enables independent regulation of active and reactive power, mitigating power oscillations and minimising grid current THD induced by onshore grid voltage deviations. The simulations in PSCAD/EMTDC and hardware-in-loop (HIL) experiments using OPAL-RT demonstrate that the proposed control strategy significantly reduces power oscillations while maintaining a grid current THD of 1.47% up to 50% unbalance in onshore grid voltage.

Neurophysiological correlates of ketamine-induced dissociative state in bipolar disorder: insights from real-world clinical settings.

Ketamine, a dissociative compound, shows promise in treating mood disorders, including treatment-resistant depression (TRD) and bipolar disorder (BD). Despite its therapeutic potential, the neurophysiological mechanisms underlying ketamine's effects are not fully understood. This study explored acute neurophysiological changes induced by subanesthetic doses of ketamine in BD patients with depression using electroencephalography (EEG) biomarkers. A cohort of 30 BD (F = 12) inpatients with TRD undergoing ketamine treatment was included in the study. EEG recordings were performed during one of the ketamine infusions with doses ranging from 0.5 to 1 mg/kg, and subjective effects were evaluated using the Clinician-Administered Dissociative States Scale (CADSS). Both rhythmic and arrhythmic features were extrapolated from the EEG signal. Patients who exhibited a clinical response to ketamine treatment within one week were classified as early responders(ER), whereas those who responded later were categorized as late responders (LR). Ketamine reduced low-frequency spectral power density while increasing gamma oscillatory power. Additionally, ketamine flattened the slope of the power spectra, indicating altered scale-free dynamics. Ketamine also increased brain signal entropy, particularly in high-frequency bands. Notably, LR exhibited greater EEG changes compared to ER, suggesting endophenotypic differences in treatment sensitivity. These findings provide valuable insights into the neurophysiological effects of ketamine in BD depression, highlighting the utility of EEG biomarkers for assessing ketamine's therapeutic mechanisms in real-world clinical settings. Understanding the neural correlates of ketamine response may contribute to personalized treatment approaches and improved management of mood disorders.

Basal forebrain innervation of the amygdala: an anatomical and computational exploration

Theta oscillations of the mammalian amygdala are associated with processing, encoding and retrieval of aversive memories. In the hippocampus, the power of the network theta oscillation is modulated by basal forebrain (BF) GABAergic projections. Here, we combine anatomical and computational approaches to investigate if similar BF projections to the amygdaloid complex provide an analogous modulation of local network activity. We used retrograde tracing with fluorescent immunohistochemistry to identify cholinergic and non-cholinergic parvalbumin- or calbindin-immunoreactive BF neuronal subgroups targeting the input (lateral and basolateral nuclei) and output (central nucleus and the central bed nucleus of the stria terminalis) regions of the amygdaloid complex. We observed a dense non-cholinergic, putative GABAergic projection from the ventral pallidum (VP) and the substantia innominata (SI) to the basolateral amygdala (BLA). The VP/SI axonal projections to the BLA were confirmed using viral anterograde tracing and transsynaptic labeling. We tested the potential function of this VP/SI-BLA pathway in a 1000-cell biophysically realistic network model, which incorporated principal neurons and three major interneuron groups of the BLA, together with extrinsic glutamatergic, cholinergic, and VP/SI GABAergic inputs. We observed in silico that theta-modulation of VP/SI GABAergic projections enhanced theta oscillations in the BLA via their selective innervation of the parvalbumin-expressing local interneurons. Ablation of parvalbumin-, but not somatostatin- or calretinin-expressing, interneurons reduced theta power in the BLA model. These results suggest that long-range BF GABAergic projections may modulate network activity at their target regions through the formation of a common interneuron-type and oscillatory phase-specific disinhibitory motif.

Sandwiched dual seed sources master oscillator power amplifier quantum cascade lasers.

To facilitate the development of compact multi-wavelengths lasers, we reported the experimental demonstration of dual seed sources master oscillator power amplifier (MOPA) QCLs. The devices were based on a sandwich configuration consisting of dual seed sources and a central power amplifier. Emission spectrum of devices was modulated by switching seed sources. By utilizing this design, two room temperature continuous-wave output power of 46 mW and 30 mW were obtained at modes 7.35 µm and 7.38 µm, respectively. The problem of power difference at the dual wavelengths have been improved. The two target modes were fine-tuning through current with tuning coefficients -0.0131 cm-1mA-1 and -0.0122 cm-1mA-1, respectively. The reported compact quantum cascade lasers with dual seed sources and fine-tuning characteristics are significant for developing miniaturized and on-chip sensing systems with multi-gas accurate detection.

Common neocortical and hippocampal correlates of Performance errors in a timing task.

We aimed to identify the neuronal correlates of performance errors in a difficult timing task. Male rats were trained to seek rewards and avoid shocks depending on the position of photic conditioned stimuli (CS-R and CS-S, respectively). Then, they were exposed to conflict trials where they had to time the interval between the CS-R and CS-S to obtain rewards while avoiding footshocks. There were pronounced individual differences in behavioral strategies on conflict trials. When presented with a CS-S, some rats quickly left the shock sector, forsaking the option of earning a reward, and rarely got shocked. Others earned rewards by delaying avoidance based on the interval between the CS-R and CS-S but were shocked more often. The probability rats would fail a given trial was not stable across trials as rats engaged in incorrect trial runs that were longer than expected by chance. Since this finding suggested that rats shift between two quasi-stable processing modes, we next examined the neuronal correlates of errors. Incorrect trials coincided with reduced firing rates in CA1 and sensory cortical neurons. Moreover, trial-to-trial variations in the firing rates of simultaneously recorded neurons were more strongly correlated on error than correct trials. Last, the power of low frequency local field potential oscillations was higher during incorrect trials. The finding that the neuronal correlates of correct and error trials are similar in the hippocampus and neocortex lead us to hypothesize that they depend on changes in the activity of common afferents, such as neuromodulatory inputs.Significance statement We studied the neuronal correlates of performance errors in a task where rats had to time the interval between reward- and shock-predicting stimuli to obtain rewards while avoiding footshocks. Rats engaged incorrect trial runs that were longer than expected by chance, suggesting that they shift between two quasi-stable modes of processing. Error trials coincided with a drop in the firing rates of CA1 and sensory cortical neurons. Trial-to-trial variations in the firing rates of simultaneously recorded CA1 and cortical neurons were more strongly correlated on error than correct trials. The finding that the correlates of correct and error trials are similar in the hippocampus and neocortex suggest that they depend on common changes in neuromodulator levels.

Characterizing autogenous and reactive obsessions using theta and beta oscillations under inhibitory demands.

Obsessive-Compulsive Disorder (OCD) is a heterogenous mental health condition that causes significant impairment and is often associated with poor treatment outcomes. The aim of the current study was to examine the association between electroencephalographic (EEG) oscillatory power during inhibitory task performance and obsessive-compulsive symptoms (OCS). OCS was assessed using the well-established the Autogenous-Reactive Obsession (AO-RO) model as the main framework to address its heterogeneous clinical manifestations. The severity of AO and RO, as primary outcome measures, was indexed using the Revised Obsessive Intrusion Inventory (ROII). Cognitive- and behavioral inhibition (CI; BI) tasks were administered while EEG data were recorded from an analogue sample of 63 undergraduate students with OCS assessed along a dimensional spectrum. Oscillatory power was computed from frontal-central electrodes Fz and Cz for theta and beta frequency bands, using event-related spectral perturbations (ERSPs), which were entered into two hierarchical linear regression models to predict the severity of AO and RO, respectively, while controlling for covariates (i.e., sex, age, ethnicity, anxiety, depression, worry, and behavioral task performance). Theta power during CI (Theta-CI) was the only significant EEG predictor of AO severity, whereas beta power during BI (Beta-BI) was the only significant EEG predictor of RO severity. These results suggest that AO severity is primarily associated with an overactive neural correlate of cognitive control, whereas RO severity is primarily associated with an overactive neural correlate of behavioral cancellation. These results agree with previous literature suggesting overactive band power representing the dysfunction within OCD. Theta-CI and Beta-BI may serve as potential biomarkers differentially associated with AO and RO among undiagnosed individuals displaying varying levels of OCS, which warrants further investigation.

Multimodal neurophenomenology of advanced concentration absorption meditation: An intensively sampled case study of Jhana.

Using a combination of fMRI, EEG, and phenomenology ratings, we examined the neurophenomenology of advanced concentrative absorption meditation, namely jhanas (ACAM-J), in a practitioner with over 23,000 h of meditation practice. Our study shows that ACAM-J states induce reliable changes in conscious experience and that these experiences are related to neural activity. Using resting-state fMRI functional connectivity, we found that ACAM-J is associated with decreased within-network modularity, increased global functional connectivity (GFC), and desegregation of the default mode and visual networks. Compared to control tasks, the ACAM-J were also related to widespread decreases in broadband EEG oscillatory power and increases in Lempel-Ziv complexity (LZ, a measure of brain entropy). Some fMRI findings varied by the control task used, while EEG results remained consistent, emphasizing both shared and unique neural features of ACAM-J. These differences in fMRI and EEG-measured neurophysiological properties correlated with specific changes in phenomenology - and especially with ACAM-J-induced states of bliss - enriching our understanding of these advanced meditative states. Our results show that advanced meditation practices markedly dysregulate high-level brain systems via practices of enhanced attention to sensations, corroborating recent neurocognitive theories of meditation as the deconstruction of the brain's cortical hierarchy. Overall, our results suggest that ACAM-J is associated with the modulation of large-scale brain networks in both fMRI and EEG, with potential implications for understanding the mechanisms of deep concentration practices and their effects on subjective experience.

Measuring Realistic Emotional Perception With EEG: A Comparison of Multimodal Videos and Naturalistic Scenes.

Emotional experiences involve dynamic multisensory perception, yet most EEG research uses unimodal stimuli such as naturalistic scene photographs. Recent research suggests that realistic emotional videos reliably reduce the amplitude of a steady-state visual evoked potential (ssVEP) elicited by a flickering border. Here, we examine the extent to which this video-ssVEP measure compares with the well-established Late Positive Potential (LPP) that is reliably larger for emotional relative to neutral scenes. To address this question, 45 participants viewed 90 matched pairs of realistic videos and scenes. Consistent with prior work, reduced 7-8 Hz ssVEP amplitude was evident during emotional relative to neutral videos. However, this reduction in power was not specific to the driving frequency of 7.5 Hz, and in fact, Fourier transformation analyses limited to 7.5 Hz were not modulated by video content. Still, at the group level, the video-driven reductions in 7-8 Hz power and LPP modulation by scenes produced similarly large valence effects, and both measures strongly correlated with arousal ratings. Consistent with previous research, the scene-LPP was sensitive to specific emotional contents (erotica and gore) somewhat inconsistent arousal ratings. In contrast, the video-driven oscillation modulation did not show this content sensitivity and was better explained by individual arousal ratings per video clip. In sum, these results show that the 7.5 Hz flickering-border paradigm does not index emotional engagement with video stimuli, yet emotional videos do evoke robust decreases in 3-10 Hz oscillatory power that is somewhat distinct from emotional modulation of the scene-evoked LPP. Matched emotional video and scenes evoke large EEG responses compared with neutral content within-participant. Our findings align with previous research indicating that video modulation of power around the evoked 7.5 Hz ssVEP frequency (7-8 Hz) serves as a reliable emotional measure. However, further analyses reveal that this effect is attributable to a general decrease in power across the 3-10 Hz frequency range.

Optimization strategy of power oscillations suppression considering ESS converter capacity limit

Optimization strategy of power oscillations suppression considering ESS converter capacity limit

Contribution to power oscillations damping of inverter based resources

Contribution to power oscillations damping of inverter based resources

Enhancement of PV array performance through reconfiguration with P&O MPPT for shading resilience

This study investigates the impact of static PV array reconfiguration techniques integrated with the Perturb and Observe (P&O) MPPT algorithm on power output and system performance under partial shading conditions (PSC). The research compares various reconfiguration methods, including conventional total cross-tied (TCT) and advanced methods like Magic Square (MMS) and Novel Shade Dispersion (NSD), through MATLAB simulations and real-time hardware experiments using a 5 W PV panel. The results indicate that MMS and NSD reconfigurations achieve the highest power outputs and minimal steady-state power oscillations across different shading scenarios. Specifically, MMS shows consistent efficiency levels of 97.49% under HZ and 97.8% under RM shading conditions, with tracking efficiencies of 97.02% and 97.58% under LBC and TC, respectively. Experimental validation confirms that MMS reconfiguration, combined with the P&O algorithm, results in the lowest steady-state oscillations, enhancing overall system performance and providing a robust solution for optimising PV systems under varying environmental conditions.

Enhancing power system stability in wind-integrated networks through coordinated fuzzy logic control of PSS and POD for sustainable energy futures

In the face of challenges posed by increasing wind power integration, penetrations, SG replacement with Wind Farm (WF), and significant disturbances and their impact on small signal and transient stability. This paper presents a novel solution employing coordinated control of Power System Stabilisers (PSS) and Power Oscillation Dampers (POD). The stochastic nature of wind power and nonlinear wind speed behaviour creates complexities and is exacerbated by severe disturbances. Traditional PSS and POD designs struggle with these non-linearities. An intelligent coordinated control strategy based on a Fuzzy Logic Controller (FLC) is proposed to address this issue. The FLC synchronises and coordinates PSS and POD, enhancing stability by dampening Low-Frequency Oscillations (LFOs) and mitigating undamped fluctuations even under extreme conditions. Sensitivity analysis identifies key LFOs affecting stability, enabling optimal control gain selection. RT-LAB experimental platform on the IEEE-9 bus system validates the approach, showcasing superior performance compared to individual methods. The results underscore the robustness and reliability of FLC coordination, ensuring enhanced system stability in practical applications.

Coherent Changes in Neural Motor Network Activity during Levodopa-Induced Dyskinesia in a Rat Model of Parkinson's Disease.

Long-term use of levodopa, a metabolic precursor of dopamine (DA) for alleviation of motor symptoms in Parkinson's disease (PD), can cause a serious side effect known as levodopa-induced dyskinesia (LID). With the development of LID, high-frequency gamma oscillations (~100 Hz) are registered in the motor cortex (MCx) in patients with PD and rats with experimental PD. Studying alterations in the activity within major components of motor networks during transition from levodopa-off state to dyskinesia can provide useful information about their contribution to the development of abnormal gamma oscillations and LID. Freely moving rats with unilateral 6-hydroxydopamine hydrobromide (6-OHDA)-induced nigral DA cell lesions were administered a high dose of levodopa for 7 days. Local field potentials (LFPs) and neuronal activity were recorded from electrodes implanted in the motor cortex (MCx), ventromedial nucleus of the thalamus (VM), and substantia nigra pars reticulata nucleus (SNpr). Levodopa reduced the power of beta oscillations (30-36 Hz) associated with bradykinesia in PD rats in three divisions of the motor neural network (MCx, VM, and SNpr) and prompted subsequent emergence of robust high-frequency gamma oscillations (80-120 Hz) in VM and MCx, but not SNpr, LFPs. Gamma oscillations were strongly associated with the occurrence of abnormal involuntary movements (AIMs) and accompanied by an increase in spiking rates in the VM and MCx and enlarged spike-LFP synchronization with cortical gamma oscillations (68% in the VM and 34% in the MCx). In contrast, SNpr LFPs did not exhibit gamma oscillations during LID, and neuronal activity in most recordings (87%) was largely decreased and not synchronized with VM or MCx LFPs. Administration of the antidyskinetic drug 8-hydroxy-2-(dipropylamino)-tetraline hydrobromide (8-OH-DPAT) restored the initial characteristics of LFPs (30-36 Hz oscillations), rates of neuronal activity, and bradykinesia. Inhibition of VM neurons by the gamma-aminobutyric acid (GABA-A)-agonist muscimol during LID eliminated high gamma oscillations in the MCx and VM, but not dyskinesia, suggesting that gamma oscillations are not critical for the expression of AIMs. In contrast, chemogenetic activation of SNpr neurons during LID eliminated both gamma oscillations and dyskinesia. These findings suggest that levodopa treatment leads to crucial reduction of inhibitory control over motor networks due to a large decline in spiking of most SNpr GABAergic projecting neurons, which causes persistent hyperactivity in motor circuits, leading to the appearance of thalamocortical gamma oscillations and LID.

High Performance GaSb-Based DBR Laser with On-Chip Integrated Power Amplifier via Gain-Match Design

We reported on a single-longitudinal-mode operated distributed Bragg reflector laser diode emitting at 1950 nm with an on-chip integrated power amplifier. Second-order Chromium–Bragg gratings are carefully designed and fabricated at the end of the ridge waveguide. Achieving a stable single-mode operation with a large injecting current range of 800 mA from 15 °C to 40 °C. The maximum side-mode suppression ratio (SMSR) is up to 42 dB. To increase the output power, an on-chip integrated master oscillator power amplifier (MOPA) is also introduced. MOPA-DBR lasers with different matching configurations between the gain peak and Bragg wavelength are fabricated, resulting in various amplification consequences. The best device is realized with 40 nm red-shifted between Bragg wavelength and photoluminescence (PL) peak. A power amplification of 5.6 times is achieved with the maximum output power of 45 mW. Thus, we put up the feasibility and key design parameters of on-chip integrated power amplification DBR lasers towards mid-infrared.

Modification of P&amp;O Method on MPPT with PI Controller to Reduce Power Oscillation during Steady-State Condition

This paper describes an improvement of the Perturb and Observe (P&amp;O) method for maximum power-point tracking (MPPT) on a photovoltaic (PV) cell generating system. The improvement is achieved using a reference voltage () value to respond to perturbation and calculates the power change based on the response. The magnitude of is determined using the reference voltage change . A large will result in a faster tracking but increasing oscillations during steady-state conditions, while a small will reduce oscillations but slowing down the MPPT response. An adaptive value is required to reduce the oscillations which have potential to degrade the efficiency of the PV cell output. A proportional-integral (PI) controller is explored in this paper in determining the most appropriate value. The results showed that oscillations around the MPP point could be reduced by 99%.

A Robust Salp Swarm Algorithm for Photovoltaic Maximum Power Point Tracking Under Partial Shading Conditions

Currently, numerous intelligent maximum power point tracking (MPPT) algorithms are capable of tackling the global optimization challenge of multi-peak photovoltaic output power under partial shading conditions, yet they often face issues such as slow convergence, low tracking precision, and substantial power fluctuations. To address these challenges, this paper introduces a hybrid algorithm that integrates an improved salp swarm algorithm (SSA) with the perturb and observe (P&amp;O) method. Initially, the SSA is augmented with a dynamic spiral evolution mechanism and a Lévy flight strategy, expanding the search space and bolstering global search capabilities, which in turn enhances the tracking precision. Subsequently, the application of a Gaussian operator for distribution calculations allows for the adaptive adjustment of step sizes in each iteration, quickening convergence and diminishing power oscillations. Finally, the integration with P&amp;O facilitates a meticulous search with a small step size, ensuring swift convergence and further mitigating post-convergence power oscillations. Both the simulations and the experimental results indicate that the proposed algorithm outperforms particle swarm optimization (PSO) and grey wolf optimization (GWO) in terms of convergence velocity, tracking precision, and the reduction in iteration power oscillation magnitude.