ABSTRACT Active distribution network (ADN) plays a vital role in the smart power grid implementation process. Congestion prediction and avoidance are essential in ADN to prevent overloads, improve efficiency, and ensure system stability. Various existing approaches are developed to alleviate congestion in ADN, but they are unable to predict the accurate congestion and require more time to generate appropriate control commands for power dispatch. Therefore, more advanced and adaptive methods are needed for accurate congestion management in modern power systems. This work implements a deep learning algorithm combined with an efficient cryptography model to ensure secure data transmission in ADN. Initially, network characteristics are collected during data transmission from the distributed network for attack detection and preprocessed using Two‐Step Sparse Switchable Normalization (TSSN‐Net) to normalize the data and Self‐Attention‐based Imputation for Time Series (SAITS) based missing value imputation to improve quality by filling in the blanks. The preprocessed data were used to select the features using redundancy analysis and interaction weight (RAIW). The Gaussian Process Reference‐based General Regression Neural Network (GPR‐GRNN) is then used to anticipate congestion using the selected features. Once the congestion is predicted, it is avoided using the Density‐Based Congestion Control Protocol (DBCCP), which reduces the loss of packets to avoid the network congestion. After that, the gathered data are securely stored in the cloud server through linear feedback shift register encryption (LFSRE) based encryption algorithm. The proposed approach achieves an accuracy of 97.60%, PPV of 96.70%, selectivity of 97.20%, and NPV of 96.10%. The proposed approach enables significant advancement in modern power systems focusing on intelligent forecasting and uncertainty‐aware congestion management for long‐term success.

Shadow detection has become a key technology for ground-based moving target indication in video synthetic aperture radar (SAR). However, single-platform video SAR faces the issue of moving-target shadows being occluded. This paper proposes a new dynamic programming-based spatiotemporal track-before-detect (DP-ST-TBD) algorithm for moving-target shadow indication based on a distributed unmanned aerial vehicle (UAV)-borne video SAR system. First, this approach establishes a spatiotemporal cooperative shadow detection model, which extends the temporal accumulation of traditional DP-TBD to spatiotemporal accumulation by state temporal transition and spatial mapping. Second, an adaptive state transition method is proposed to address the challenge in which the fixed-state transition of traditional DP-TBD struggles with maneuvering target detection. It utilizes target’s Doppler features from heterogeneous-view range-Doppler (RD) spectra to assist in target’s shadow search within the image domain. Finally, a state shrinking–sparseness strategy is used to reduce the computational burden caused by dense states in spatiotemporal search; thus, multi-platform, multi-frame accumulation of moving-target shadows can be realized based on sparse states. The comparative experiments demonstrate that the proposed DP-ST-TBD improves shadow-detection performance through heterogeneous-view measurements while reducing the required number of frames for reliable detection compared to the conventional two-step detection method (single-platform shadow detection followed by multi-platform track fusion).

Direct power control (DPC) is attracting wide attention throughout the world outstanding to its merits of simple construction, rapid dynamic reaction, and strong robustness against parameter variations. The establishment of a switching table is the key aspect of DPC, and so far, there are various switching tables in the literature. However, they differ in the sector division and vector selection. The simulation results verify that the improved DPC using the neural network ANN method has a more satisfactory compensation effect when the grid voltage is under distorted and unbalanced circumstances, and it proves that the ANN method is correct and superior. Improved DPC achieves control of active power and its dynamic performance. The good compensation of reactive power using the adaptive ANN method results in low ripple.

We develop and analyze an adaptive spacetime finite element method for nonlinear parabolic equations of p–Laplace type. The model problem is governed by a strongly nonlinear diffusion operator that may be degenerate or singular depending on the exponent p, which typically leads to limited regularity of weak solutions. To address these challenges, we formulate the problem in a unified spacetime variational framework and discretize it using conforming finite element spaces defined on adaptive spacetime meshes. We prove the well-posedness of both the continuous problem and the spacetime discrete formulation, and establish a discrete energy stability estimate that is uniform with respect to the mesh size. Based on residuals in the spacetime domain, we construct a posteriori error estimators and prove their reliability and local efficiency. These results form the foundation for an adaptive spacetime refinement strategy, for which we prove global convergence and quasi-optimal convergence rates without assuming additional regularity of the exact solution. Numerical experiments confirm the theoretical findings and demonstrate that the adaptive spacetime finite element method significantly outperforms uniform refinement and classical time-stepping finite element approaches, particularly for problems exhibiting localized spatial and temporal singularities.

Underwater images often suffer from luminance attenuation, structural degradation, and color distortion due to light absorption and scattering in water. The variations in illumination and color distribution across different water bodies further increase the uncertainty of these degradations, making traditional enhancement methods that rely on fixed parameters, such as underwater dark channel prior (UDCP) and histogram equalization (HE), unstable in such scenarios. To address these challenges, this paper proposes a multi-operator underwater image enhancement framework with adaptive parameter optimization. To achieve luminance compensation, structural detail enhancement, and color restoration, a collaborative enhancement pipeline was constructed using contrast-limited adaptive histogram equalization (CLAHE) with highlight protection, texture-gated and threshold-constrained unsharp masking (USM), and mild saturation compensation. Building upon this pipeline, an adaptive multi-operator parameter optimization strategy was developed, where a unified scoring function jointly considers feature gains, geometric consistency of feature matches, image quality metrics, and latency constraints to dynamically adjust the CLAHE clip limit, USM gain, and Gaussian scale under varying water conditions. Subjective visual comparisons and quantitative experiments were conducted on several public underwater datasets. Compared with conventional enhancement methods, the proposed approach achieved superior structural clarity and natural color appearance on the EUVP and UIEB datasets, and obtained higher quality metrics on the RUIE dataset (Average Gradient (AG) = 0.5922, Underwater Image Quality Measure (UIQM) = 2.095). On the UVE38K dataset, the proposed adaptive optimization method improved the oriented FAST and rotated BRIEF (ORB) feature counts by 12.5%, inlier matches by 9.3%, and UIQM by 3.9% over the fixed-parameter baseline, while the adjacent-frame matching visualization and stability metrics such as inlier ratio further verified the geometric consistency and temporal stability of the enhanced features.

ABSTRACT Low earth orbit (LEO) satellite communication system can achieve global coverage and has attracted widespread attention. However, the common demand for broadband satellite spectrum resources inevitably leads to cofrequency interference between LEO and geostationary orbit (GSO) satellite communication systems. To meet interference avoidance requirements while ensuring LEO system performance, this paper proposes an adjacent satellites collaborative beam hopping–based interference avoidance technique for GSO–LEO coexistence systems. First, the adaptive demand satisfaction ratio user grouping method is developed to accommodate the energy efficiency weight factor in resource efficiency (RE). Meanwhile, a residual demand priority adjacent satellites collaborative greedy user scheduling scheme is employed to enhance LEO system capacity while avoiding severe interference to GSO systems. Subsequently, a quadratic transformation–based convex optimization power allocation is proposed to further improve LEO system RE and reduce cofrequency interference. Finally, the above steps are alternately iterated timeslot by timeslot to obtain the local optimal user scheduling strategy and power allocation scheme over the beam‐hopping period. Simulation results validate that the proposed technique effectively mitigates interference while achieving optimal performance in multiple indicators including RE.

High-frequency surface wave radar (HFSWR) with a small aperture suffers from limited azimuth resolution, which often leads to association errors and trajectory fragmentation in complex scenarios involving sea clutter and intersecting target tracks. To address this issue, we propose a multi-feature adaptive association method that integrates the target direction cosine features and motion parameters to construct an improved association gate suitable for targets in uniform linear motion. For multiple plots within the association gate, the method evaluates their similarity to the trajectory by combining multiple feature parameters such as great-circle distance and Mahalanobis distance. An adaptive weighting strategy is employed according to the trajectory state to select the most similar plot for association. For trajectories without associated plots, the method maintains them based on a motion model and Kalman predictor. Experimental results demonstrate that the trajectories generated by this method last longer than those produced by traditional association methods, confirming that the proposed approach effectively suppresses trajectory fragmentation and false tracking, thereby enhancing the continuity and reliability of HFSWR target tracking.

ABSTRACT In the context of rising urbanization, environmental degradation and the urgent need for sustainable development, adaptive reuse has become a vital strategy for extending building lifecycles while conserving resources. This study empirically assessed awareness and adoption of adaptive reuse techniques among construction professionals in Lagos State, Nigeria. A quantitative approach was employed, using structured questionnaires distributed to architects, builders, engineers and quantity surveyors. Descriptive and inferential analyses examined awareness levels and the extent of technique adoption. Findings reveal a low overall awareness, indicating limited familiarity with most adaptive reuse methods. Adoption levels mirrored this pattern, with only two strategies—conversion for rehabilitation services and contextual design strategies—showing above-average uptake. The results suggest that while some recognition exists for approaches offering immediate value, many techniques remain underexplored or poorly understood. These gaps highlight the need for capacity-building, professional training and policy interventions to promote wider implementation. The study provides the first empirical evidence in Nigeria quantifying awareness and adoption of adaptive reuse among construction professionals. By situating the research within Nigeria’s socio-economic and infrastructural realities, it offers context-specific insights and recommendations for cost-effective, environmentally conscious alternatives to demolition and new construction, bridging a critical gap in local sustainability literature.

Ensuring reliable perception under adverse weather is critical for Cyber-Physical Systems (CPS) in intelligent railway applications. LiDAR, as a key sensor, provides high-precision 3D data but suffers from severe degradation in fog due to signal scattering and attenuation, introducing noise into point clouds and disrupting CPS feedback loops. This paper proposes a CPS-oriented point cloud defogging framework that estimates fog distribution based on attenuation modeling and local density analysis. A multi-scale adaptive thresholding method is introduced, dynamically adjusting denoising parameters by combining a base multiscale factor with density-driven adaptive scaling. Dynamic neighborhood statistics and height-based corrections further refine the noise removal process, enhancing the distinction between fog noise and true object structures. Experimental evaluations on synthetic and real-world foggy datasets show that the proposed method outperforms traditional techniques such as SOR, DSOR, DROR in terms of precision, recall and structural preservation. By improving the reliability of the perception layer, our approach reinforces the overall robustness and safety of CPS-driven railway systems operating in foggy environments, providing a lightweight and adaptable solution suitable for real-time deployment.

Steganography is a method to hide confidential messages in digital media so that they are not detected by unauthorized parties. Unlike cryptography which protects the content of messages through encryption, steganography hides the message itself. One popular technique is the Least Significant Bit (LSB), which replaces the least important bit on the pixel with a secret message bit. However, conventional LSB methods such as 1-bit or 3-bit have limitations due to the compromise between insertion capacity and visual quality of the media. This study proposes an LSB-based video steganography method with an adaptive multi-bit embedding approach. This technique determines the number and position of bits that are dynamically inserted based on the local brightness and texture levels of each video frame, with Laplacian operators used to analyze both high and low textured areas. The process includes frame and audio extraction, frame-by-frame embedding, inserted video reconstruction, and decoding using video cover references. The evaluation was carried out quantitatively using the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) metrics, as well as qualitatively through visual comparison. The results showed that the adaptive multi-bit method was able to maintain visual quality with a PSNR of 45.23 dB and SSIM of 0.9424, and increased the insertion capacity by up to 2–3 times compared to the 1-bit adaptive method. Thus, this approach effectively balances imperceptibility and insertion capacity on dynamic video steganography systems.

Language learning is closely intertwined with human interaction and the identity shaping process. By nature, the ecological approach to language learning conceptualizes language not as an isolated system, but as an interactive and adaptive component of a greater system. This article examines the integration of the ecological paradigm into language education, focusing on the role of ecological, pedagogical, and individual factors that influence learners in the language environment. The focus of the article is on the linguistic adaptation of newcomers to new language and educational ecosystems, in the context of the Latvian school system after the geopolitical shifts of 2022 (the Russian invasion of Ukraine). The results of the theoretical and empirical analysis highlight the importance of localized, adaptive teaching methods that take the individual needs and previous experiences of learners into account to create the conditions for comfortable language integration. Going beyond rigid curricular approaches, the ecological perspective offers a basis for promoting sustainable and inclusive language education. The aim of this study is to explore how the ecological approach to language learning can be effectively applied to support the linguistic integration of newcomer students—particularly Ukrainian children—within the Latvian educational system.

Purpose – This study aims to develop an accurate method for predicting the daily operation of power plants to support optimal scheduling of generation and maintenance activities. Methodology – An adaptive filter based on wavelet symlet (adaplet) is applied using the Normalized Least Mean Square (NMLS) algorithm. The model adjusts its coefficients dunamically based on historical operational data to minimize prediction error. Findings – The method was tested on Indonesian power plant operation data and achieved a Mean Square Error (MSE) of 0.079. Segment-based evaluation confirmed the model’s ability to provide consistent prediction accuracy across different time frames. Originality – This research introduces a novel approach by combining wavelet symlet and adaptive filtering in the context of power plant operation prediction, which allows accurate forecasting using limited data. Research limitations – The study focuses on short-term prediction (up to 3 days ahead) and does not include external influencing factors such as weather or system demand. Only the NLMS algorithm was utilized, without comparison to other adaptive methods. Practical implications – The proposed method enables operators to generate more accurate and reliable schedules, improving overall system performance and reducing outage risks. Social implications – Enhancing the reliability of power plant operations contributes to a more stable electiricty supply, indirectly supporting public services and economic activities.

As the proportion of renewable energy generation in the power grid continues to rise, the operational state of the power system changes frequently with fluctuations in renewable power output. However, the traditional fixed-weight multi-objective reactive power optimization method lacks the necessary flexibility and adaptability, as it is unable to dynamically adjust the priority levels of different objectives based on real-time operating conditions (such as load fluctuations and changes in network structure). As a result, its optimization decisions may deviate from the system’s most urgent economic or security needs. To address this issue, this paper proposes an adaptive multi-objective reactive power optimization control method. The proposed approach formulates the objective function as the weighted sum of system active power loss and voltage deviation at the grid connection point, with weight coefficients adaptively adjusted based on the voltage deviation at the grid connection point. First, the relationship between voltage fluctuations at the offshore wind farm grid connection point and active/reactive power output is analyzed, and a corresponding reactive power allocation model is established. Second, taking into account the input–output characteristics of wind turbine generators and static var compensators, a reactive power control model is constructed. Third, considering offshore operational constraints such as power and voltage limits, a weighted variation particle swarm optimization algorithm (WVPSO) is developed to solve for the reactive power control strategy. Finally, the proposed method is validated through tests using a practical offshore wind farm as a case study. The test results demonstrate that, compared with the traditional fixed-weight multi-objective reactive power optimization approach, the proposed method can rapidly adjust the priority of each optimization objective according to the real-time grid conditions, achieving effective coordinated optimization of both active power loss and voltage at the grid connection point, and the voltage deviation is kept within 5%, even with power system fluctuations. In addition, compared with the traditional PSO algorithm, for various test situations, WVPSO exhibits above 15% improvement in solution speed and enhanced solution accuracy.

Mothers with inadequate intake of micronutrients are a serious and collective global health issue, especially in poverty stricken areas. However, the available studies in Ethiopia have been usually focused in early childhood nutrition using old statistical methods. The aim of this study is to apply multiple machine learning algorithms to construct a high fidelity predictive model and identify key predictors of Inadequate Minimum Dietary Diversity for Women among Ethiopian mothers with a child under 24 months. A weighted sample of 3,914 women from the Ethiopian Demographic Health Survey 2016 was utilized to conduct a secondary analysis of data. The outcome variable was dichotomous: Inadequate Minimum Dietary Diversity for Women or Adequate Minimum Dietary Diversity for Women. The data was divided into 20% and 80% in the testing and training respectively. We used R software version 4.5 to apply and test ML algorithms. To deal with the harsh imbalance of classes, the Adaptive Synthetic method was utilized, and robust feature selection was performed by the Boruta algorithm. An entire set of seven machine learning algorithms classifiers was trained and tested (Accuracy, Recall, F1 score, specificity, precision and AUC). Random forest algorithm (accuracy = 95.03%, sensitivity = 92.73%, precision = 97.28% F1-score = 94.94% and AUC = 98.34) was the best predictive model since it had better performance metrics on the test set. Rural residence, unprotected source of drink water, poor wealth index, no media exposure, unimproved toilet facility, no education, age, religion, and traditional method of contraceptive were the top factors to predict minimum dietary diversity of women. Machine learning models, specifically the Random forest classifier, are well-suited to predict a mother with Minimum Dietary Diversity, which provides a useful decision-supporting tool to the health officials of the populace. The results of the study suggest evidence based guidance, including the necessity of geographically concentrated interventions and the combined programs that can integrate the effects of nutrition education, family planning, and economic empowerment to help reduce the overwhelming socioeconomic and demographic risk factors to advance poor maternal dietary diversity in Ethiopia.

Abstract Traditional extended Kalman filter is a popular method in vehicle state parameters estimation. However, its performance undergoes significant degradation when encountering non-Gaussian noise or noise of uncertain statistical properties. The maximum correntropy extended Kalman filter (MCEKF) based on Gaussian kernel can handle some non-Gaussian noise scenarios, but it is still less effective in dealing with noise of uncertain statistical characteristics and is typically sensitive to manually selected kernel bandwidths. To overcome these constraints, this paper proposes a new adaptive extended Kalman filtering algorithm based on the Cauchy-kernel maximum correntropy criterion (CKMCC), referred to as the Cauchy-kernel maximum correntropy adaptive extended Kalman filter (CKMCAEKF). By integrating the CKMCC and an adaptive method, the proposed algorithm eliminates sensitivity to selection of kernel bandwidth and enhances robustness against non-Gaussian noise and noise of uncertain statistical characteristics. Moreover, the robustness of the proposed method is theoretically proven. The effectiveness of the proposed method is validated through co-simulations on the MATLAB/Simulink and CarSim platforms. CKMCAEKF achieves superior estimation accuracy for vehicle state parameters to other existing methods under non-Gaussian measurement noise scenarios and scenarios of changes in statistical characteristics of measurement noise caused by sudden events.

This study aims to analyze the application of the time series method using the Moving Average approach to forecast raw material requirements at UD. Tunas Baru, Gunungsitoli City. The company has traditionally managed its inventory without a systematic calculation basis, resulting in fluctuating purchases and usage that cause overstock and shortages. This research employed a descriptive quantitative method using data on wood board purchases and usage from January to December 2024.The results show that the Moving Average (3-period) method provides a general overview of raw material demand trends, although it remains less accurate in capturing sharp fluctuations. The Mean Squared Error (MSE) value was 27,338 (sheets²), indicating a relatively high deviation between actual and forecasted values.These findings suggest that time series-based forecasting can serve as a preliminary foundation for raw material planning at UD. Tunas Baru. Applying an appropriate forecasting method can help the company minimize inventory imbalance risks and improve production efficiency. It is recommended that the company implement a data-based inventory management system and consider more adaptive forecasting methods such as Weighted Moving Average or Exponential Smoothing.

ABSTRACT Satellite aerosol products provide valuable large-scale atmospheric information for environment and climate research. Nevertheless, limitations due to cloud contamination and retrieval assumptions often result in significant gaps in Aerosol Optical Depth (AOD) observations, diminishing their representativeness and utility. Therefore, we propose an adaptive spatiotemporal reconstruction method (Spatio-Temporal Reconstruction with Ecw and Auds Method, STREAM) based solely on a single data source, which integrates Empirical Correlation Weighting (ECW) for interpolation with Adaptive Up/Down Scaling (AUDS) for seamless reconstruction. This method was applied to Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue (DB) AOD over the North China Plain (NCP) from 2002 to 2023 at a 0.05° resolution. Case comparisons demonstrate that STREAM efficiently fills data gaps, and the STREAM AOD presents strong concordance with both the DB AOD and reference datasets. Cross-validation indicates that as the missing rate rises, the correlation (R) between the STREAM AOD and the DB AOD decreases, while Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) increase. Validation against AERONET data shows that STREAM AOD achieves an R value of 0.88, RMSE of 0.29, and MAE of 0.18 for STREAM AOD, with 52.39% of the data falling within the expected error range. Compared to Long-term Gap-free High-resolution Air Pollutants (LGHAP) AOD, our approach reveals minor discrepancies in values and spatial distribution despite relying on a single data source. The robust performance of STREAM AOD in the NCP highlights potential applicability to utilize in broader regions as well as other atmospheric remote sensing products.

The 5-point Deauville score (DS) assesses end-of-treatment (EOT) response on PET/CT in diffuse large B-cell lymphoma patients, categorizing scans as 'positive' or 'negative' for complete metabolic response. However, the positive predictive value (PPV) is suboptimal at 60%. We evaluated whether quantitative PET parameters combined with clinical data could improve prediction of treatment failure in EOT PET-positive patients. Baseline and EOT PET/CT scans of 138 DS4-5 patients were analyzed. Lesions were segmented using a semi-automated adaptive method (SUV4.0 or MV3). PET parameters, including total metabolic tumor volume (TMTV), number of lesions (NOL), tumorSUV/liverSUV-ratio (TLR), the maximum distance between the largest and any other lesion (DmaxBulk), and changes over time, were obtained. Two Cox regression models predicted 2-year progression-free survival. Clinical data were combined with EOT PET in model 1, and baseline, EOT and delta values in model 2. After internal bootstrapping, models were evaluated for classification using different risk-of-progression cutoffs. Sensitivity, specificity, PPV and negative predictive values (NPV) were determined. Using forward selection, model 1 comprised two variables: the NOL and the tumorSUVpeak/liverSUVmean (TLRpeakmean) at EOT (AIC=690.072, c-index=0.747). Model 2 incorporated NOL, TLRpeakmean (EOT) and baseline SUVmean (AIC=687.064, c-index=0.762). The PPV improved to over 85% without compromising the NPV. False positives dropped from 54 (39%, by DS) to 9 (7%) and 6 (4%) for models 1 and 2, respectively. Adding baseline features did not notably impact the models' performance. Our models could support more accurate response-adapted treatment decisions, reducing unnecessary subsequent false positive-directed treatments to just 7%.

Abstract Aiming at the problems of insufficient local geometric representation, low efficiency of multiscale feature fusion and noise sensitivity of point cloud registration in complex scenes, this paper proposes an adaptive deep learning framework based on iterative weighted SVD with dynamic multi-scale attention mechanism (IWSVD-DMSA). A dynamic convolutional kernel with adaptive inputs is generated through a hypernetwork, and combined with multi-scale channel attention to achieve hierarchical fusion of local-global features, which enhances the ability to characterize complex geometric structures; a coordinate-feature joint attention model is designed to filter high-confidence neighborhoods using a hybrid distance metric to improve the consistency of the local structure; a multitasking supervisory strategy is designed to filter the reliable corresponding pairs of points dynamically and optimize the transformation parameters to reduce the noise interference through an iteratively weighted SVD. Experiments on the ModelNet40 dataset show that, when a random noise test set (SNR=10dB) is added, the proposed method's registration accuracy (RMSE(R)=0.078, RMSE(t)=0.0018) is significantly better than the current best method. Furthermore, a group of experiments prove that the method can achieve complete and accurate 3D point cloud registration for high dynamic range surfaces with any complex structure compared to other methods. Ablation experiments validate the effectiveness of the module.

Islanding detection remains a critical challenge in grid-connected photovoltaic (PV) systems, as failure to detect islanding conditions can compromise power quality, safety, and system stability. Existing active frequency drift (AFD) methods suffer from two major unresolved limitations: a large non-detection zone (NDZ) and fixed perturbation parameters that cannot adapt to changing grid and load conditions. Despite extensive studies on AFD, no existing method adaptively adjusts its perturbation parameters in response to grid dynamics, creating a significant research gap. To address this specific gap, this work introduces a novel AI-driven adaptive AFD method that uses reinforcement learning to dynamically optimize perturbation behavior to eliminate the NDZ and enhance stability. The proposed method employs Reinforcement Learning (RL) to optimize the chopping fraction (Cf) and an enhanced correction factor (Cr'), which is updated based on the rate of change of frequency (df/dt) and Cf to minimize NDZ while maintaining grid stability. The RL agent is trained using a reward-based learning strategy to achieve fast, accurate islanding detection while minimizing unnecessary disturbances. Simulation and experimental results show that the proposed adaptive AFD achieves islanding detection within 0.12-0.17s (compared with 0.2-0.5s for standard AFD) and reduces the NDZ to below 1% (compared with 10-15% for conventional AFD), while keeping total harmonic distortion (THD) within ≤ 2%. These quantified improvements demonstrate substantial gains in detection speed, robustness, and power quality. Experimental validation across different PV configurations confirms the scalability and reliability of the method. The proposed technique, therefore, offers a promising solution for modern smart grids by ensuring compliance with IEEE Std. 929 islanding detection requirements while maintaining high power quality and system stability.