Network Disturbances Research Articles

Finite-time trajectory tracking control of quadrotor UAVs based on neural network disturbance observer and command filter

The paper proposes a novel finite-time control strategy for quadrotor UAV trajectory tracking using a neural network disturbance observer and a command filter. This method is used to address input saturation and disturbances, ensuring that the UAV can accurately follow the desired trajectory in finite time. The neural network disturbance observer is crucial for approximating external disturbance signals within a finite time, while the finite-time backstepping scheme accelerates the convergence of tracking errors. The command filtering technique is employed to avoid the complex derivation of virtual control laws, simplifying the controller design. The importance of this method lies in its ability to achieve fast, disturbance-resistant trajectory tracking for UAVs, making the control system more robust in practical applications. Simulations were conducted, showing that the proposed control strategy enables the quadrotor UAV to track its desired trajectory effectively, with improved anti-jamming capability. Both filtering and observation errors converged to the equilibrium point, validating the effectiveness of the approach. However, internal factors like actuator failure were not considered, pointing to future work in refining the method and applying it in real-world UAV experiments.

An Open-Source Julia Package for RMS Time-Domain Simulations of Power Systems

This paper presents RMSPowerSims.jl, an open-source Julia package for the time-domain simulation of power systems. The package is designed to be used in conjunction with PowerModels.jl, a widely used Julia package for power system optimization. RMSPowerSims.jl provides a framework for the simulation of power systems in the time domain, allowing for the study of transient stability, frequency stability, and other dynamic phenomena. The package is designed to be intuitive and flexible, allowing users to easily define custom models for network components and disturbances, while also providing a range of pre-constructed models for common power system components. RMSPowerSims.jl simplifies the process of performing RMS simulations on power system models developed using the PowerModels.jl ecosystem, and provides an easy-to-use modeling that reduces the barrier to entry for new users wishing to perform RMS simulations. The accuracy of the package is verified against DIgSILENT PowerFactory for short-circuit and load-increase disturbances, using the New England 39-bus system. The active power generation delivered by several generators in the network, and the voltage magnitudes of selected busbars are analyzed and noted to be in close agreement with those obtained using PowerFactory. The computational performance of the package is compared to that of PowerFactory and is found to be comparable for load-step simulations; however, PowerFactory is found to be considerably faster for short-circuit simulations. As computational performance is not a priority at this stage of development, this is expected, and speed optimization is planned for future work. RMSPowerSims.jl is available under an open-source license and can be downloaded from GitHub.

Energy Associated With Dynamic Network Changes in Patients With Multiple Sclerosis and Cognitive Impairment.

Patients with multiple sclerosis (MS) often experience cognitive impairment, and this is related to structural disconnection and subsequent functional reorganization. It is unclear how specific patterns of functional reorganization might make it harder for cognitively impaired (CI) patients with MS to dynamically adapt how brain regions communicate, which is crucial for normal cognition. We aimed to identify dynamic functional network patterns that are relevant to cognitive impairment in MS and investigate whether these patterns can be explained by altered energy costs. Resting-state functional and diffusion MRI was acquired in a cross-sectional design, as part of the Amsterdam MS cohort. Patients with clinically definitive MS (relapse-free) were classified as CI (≥2/7 domains Z < -2), mildly CI (MCI) (≥2/7 domains Z < -1.5), or cognitively preserved (CP) based on an expanded Brief Repeatable Battery of Neuropsychological Tests. Functional connectivity states were determined using k-means clustering of moment-to-moment cofluctuations (i.e., edge time series), and the resulting state sequence was used to characterize the frequency of transitions. Control energy of the state transitions was calculated using the structural network with network control theory. Imaging and cognitive data were available for 95 controls and 330 patients (disease duration: 15 years; 179 CP, 65 MCI, and 86 CI). We identified a "visual network state," "sensorimotor network state," "ventral attention network state," and "default mode network state." CI patients transitioned less frequently between connectivity states compared with CP (β = -5.78; p = 0.038). Relative to the time spent in a state, CI patients transitioned less from a "default mode network state" to a "visual network state" (β = -0.02; p = 0.004). The CI patients required more control energy to transition between states (β = 0.32; p = 0.007), particularly for the same transition (β = 0.34; p = 0.049). This study showed that it costs more energy for MS patients with cognitive impairment to dynamically change the functional network, possibly explaining why these transitions occur less frequently. In particular, transitions from a default mode network state to a visual network state were relevant for cognition in these patients. To further study the order of events leading to these network disturbances, future work should include longitudinal data across different disease stages.

Frontoparietal Structural Network Disconnections Correlate With Outcome After a Severe Stroke.

Structural disconnectome analyses have provided valuable insights into how a stroke lesion results in widespread network disturbances and how these relate to deficits, recovery patterns, and outcomes. Previous analyses have primarily focused on patients with relatively mild to moderate deficits. However, outcomes vary among survivors of severe strokes, and the mechanisms of recovery remain poorly understood. This study assesses the association between lesion-induced network disconnection and outcome after severe stroke. Thirty-eight ischaemic stroke patients underwent MRI brain imaging early after stroke and longitudinal clinical follow-up. Lesion information was integrated with normative connectome data to infer individual disconnectome profiles on a localized regional and region-to-region pathway level. Ordinal logistic regressions were computed to link disconnectome information to the modified Rankin Scale after 3-6 months. Disconnections of ipsilesional frontal, parietal, and temporal cortical brain areas were significantly associated with a worse motor outcome after a severe stroke, adjusted for the initial deficit, lesion volume, and age. The analysis of the underlying pathways mediating this association revealed location-specific results: For frontal, prefrontal, and temporal brain areas, the association was primarily driven by relatively sparse intrahemispheric disconnections. In contrast, the ipsilesional primary motor cortex, the dorsal premotor cortex, and various parietal brain regions showed a remarkable involvement of either frontoparietal intrahemispheric or additionally interhemispheric disconnections. These results indicate that localized disconnection of multiple regions embedded in the structural frontoparietal network correlates with worse outcomes after severe stroke. Specifically, primary motor and parietal cortices might gain particular importance as they structurally link frontoparietal networks of both hemispheres. These data shed novel light on the significance of distinct brain networks for recovery after a severe stroke.

Robust control of uncertain asymmetric hysteretic nonlinear systems with adaptive neural network disturbance observer

In this article, a novel command-filtered adaptive control scheme is proposed for uncertain asymmetric hysteretic nonlinear systems with unknown external disturbances, where the hysteresis nonlinearities are described by an asymmetric Bouc–Wen model. Firstly, the hysteresis model uncertainties are considered, a robust hysteresis state observer is constructed to compensate the asymmetric hysteresis nonlinearity. Secondly, both tracking errors and prediction errors are employed to develop the adaptive neural networks (NNs) for approximating unknown nonlinear functions of the system, where the acquisition of the prediction error avoids the need to differentiate the state measurements. Then, a nonlinear disturbance observer with the filtered control input and system states is constructed to estimate a total disturbance resulting from both the NN approximation errors and the external disturbances. With the help of filtering differentiators, a command-filtered backstepping control law is designed by using the adaptive NN approximators, the disturbance observers, and the hysteretic compensator. The effects of the filtering errors, the disturbance estimation errors, and the hysteresis compensation error on the closed-loop stability are rigorously analyzed. Finally, the proposed control algorithm is applied to a piezoelectric micro-displacement servo system, the real-time experimental results indicate that the relative average error and the relative maximal error of the sinusoidal trajectory tracking are 0.04% and 0.06%, respectively. Compared with the existing adaptive robust control algorithm, a significant improvement on the tracking accuracy is achieved.

A Review Paper on IPv4 and IPv6: A Comprehensive Survey

Even though more customers are regularly coming to the Internet, IPv4 addresses have been reduced by the Internet Assigned Numbers Authority (IANA) and have been deactivated in domain name registries (RIRs). IPv6, being the sole important next-generation Internet protocol, has yet to be fully developed and deployed, owing to the lack of a scheme that might address the transfer of IPv4 resources to IPv6 networks as well as collective communication between the two incompatible protocols. The Transmission Control Protocol/Internet protocol version 4 (TCP/IPv4) addresses have been reported as being on the verge of collapsing, while the next generation Internet Protocol version 6 (IPv6) is being identified on a regular basis. Among other advantages, IPv6 provides a significantly wider address space, better address design, and more security. IPv6 distribution necessitates a thorough and meticulous setup in order to avoid network disturbance and reap the benefits of IPv6. Because of the problems with IPv4, IPv6 is currently becoming increasingly popular among organizations, businesses, and Internet Service Providers (ISP). This paper we will explores the evolution of Internet Protocol version 4 (IPv4), its key features, challenges, and limitations, and examines how Internet Protocol version 6 (IPv6) addresses these issues. Additionally, we will highlight the key differences between the two protocols and discuss the transition process from IPv4 to IPv6.

Adaptive robust fault-tolerant control of spinning tether system for space debris removal

The Spinning Tether System (STS) is currently recognized as one of the most effective and ideal platforms for removing space debris. It offers several advantages, including a non-contact mechanism, rapid deorbit capabilities, stable spinning, and a short preparation time for subsequent missions. The system primarily depends on the thrusters of the main spacecraft and the capture device for its spin-up process. Although modern technology and quality management methods strive to minimize thruster failures, the capture of debris significantly affects the STS. This impact can induce tether oscillations and cause actuator failures, thereby destabilizing the spin-up process. Given these challenges, this paper explores the design of fault-tolerant control strategies to enhance system reliability during the post-capture spin-up phase. Initially, a dynamic model using quaternions as generalized coordinates was established based on the constrained Lagrange equation. This model facilitated a detailed analysis of potential thruster failure modes. Subsequently, the optimal trajectory for the spin-up process was planned using the pseudo-spectral method, ensuring efficiency and safety in control execution. To address the identified vulnerabilities, the study introduces an adaptive robust fault-tolerant controller. This controller, based on a BLF and integrated with a nonlinear neural network disturbance observer, is designed to operate effectively under fault conditions. Numerical simulations were conducted to validate the performance of the controller, demonstrating its capability to maintain stable control of the system, both after detecting a fault signal and following an actuator failure.

Insights from Bibliometric Analysis of Epilepsy Research in Morocco between 1969 and 2024

Epilepsy is a critical neurological condition characterized by the disturbance of the excitatory neuron network, resulting in recurrent seizures. It is a serious health problem requiring intensive attention. The present study was designed to use bibliometric analysis to assess scientific research output on pharmacogenetics in epilepsy and drug-resistant epilepsy in Morocco during 1969–2024. The main objective of this study was to analyze scientific production and research development and to introduce available solutions to manage the elevating burden of epilepsy. The strategy approach comprised a suitable search scheme to collect publications related to epilepsy using the Scopus database. All the documents retrieved were analyzed to determine several parameters, including the number of publications annually, type of documents, institution and author productivity, and relevant sources. Studies on pharmacogenetics and drug-resistant epilepsy in Morocco are limited. The findings revealed that the number of articles has increased during the last decade. Publications remain scarce, especially on pharmacogenetics and drug-resistant epilepsy, and the most widely available publications include articles on epilepsy, seizures, mental illness, and anticonvulsant agents. Based on the search strategy, a bibliometric analysis identified potential subjects for further study. Consequently, research on epilepsy is essential to fully understand the condition, but it remains insufficient, and further studies are required, particularly on the pharmacogenetics of epilepsy.

Model reference adaptive control of electro-hydraulic servo system based on RBF neural network and nonlinear disturbance observer

Model reference adaptive control of electro-hydraulic servo system based on RBF neural network and nonlinear disturbance observer

Tracking control and neural network disturbance observer for a 6 DoF underwater welding robot

This article addresses the control of an underwater welding robot in 3D space. A control approach combining a finite time sliding mode controller for position and orientation control and feedback linearization for controlling one degree of freedom of the welding arm is adopted. The utilization of underwater robots in marine environments is often hindered by uncertainties and disturbances induced by ocean waves and currents, resulting in decreased accuracy and operational disruptions. To overcome these challenges, a novel observer based on a radial basis function neural network is developed to enhance the performance of the underwater welding robot. The neural network’s weights are optimized using the Lyapunov method within the control law framework. Through simulations, the article evaluates the observer’s efficacy in accurately tracking reference trajectories in the presence of uncertainties. The results underscore the significant contribution of this estimator in mitigating uncertainties and disturbances, thereby substantially improving the overall performance and operational reliability of underwater welding robots. The control strategies and observer design presented in this study pave the way for enhanced accuracy, stability, and efficiency in complex underwater welding operations.

The non-canonical bivalent gene Wfdc15a controls spermatogenic protease and immune homeostasis.

Male infertility can be caused by chromosomal abnormalities, mutations and epigenetic defects. Epigenetic modifiers pre-program hundreds of spermatogenic genes in spermatogonial stem cells (SSCs) for expression later in spermatids, but it remains mostly unclear whether and how those genes are involved in fertility. Here, we report that Wfdc15a, a WFDC family protease inhibitor pre-programmed by KMT2B, is essential for spermatogenesis. We found that Wfdc15a is a non-canonical bivalent gene carrying both H3K4me3 and facultative H3K9me3 in SSCs, but is later activated along with the loss of H3K9me3 and acquisition of H3K27ac during meiosis. We show that WFDC15A deficiency causes defective spermiogenesis at the beginning of spermatid elongation. Notably, depletion of WFDC15A causes substantial disturbance of the testicular protease-antiprotease network and leads to an orchitis-like inflammatory response associated with TNFα expression in round spermatids. Together, our results reveal a unique epigenetic program regulating innate immunity crucial for fertility.

Postencephalitic Parkinsonism: Unique Pathological and Clinical Features-Preliminary Data.

Postencephalitic parkinsonism (PEP) is suggested to show a virus-induced pathology, which is different from classical idiopathic Parkinson's disease (PD) as there is no α-synuclein/Lewy body pathology. However, PEP shows a typical clinical representation of motor disturbances. In addition, compared to PD, there is no iron-induced pathology. The aim of this preliminary study was to compare PEP with PD regarding iron-induced pathology, using histochemistry methods on paraffin-embedded post-mortem brain tissue. In the PEP group, iron was not seen, except for one case with sparse perivascular depositions. Rather, PEP offers a pathology related to tau-protein/neurofibrillary tangles, with mild to moderate memory deficits only. It is assumed that this virus-induced pathology is due to immunological dysfunctions causing (neuro)inflammation-induced neuronal network disturbances as events that trigger clinical parkinsonism. The absence of iron deposits implies that PEP cannot be treated with iron chelators. The therapy with L-Dopa is also not an option, as L-Dopa only leads to an initial slight improvement in symptoms in isolated cases.

Radial Basis Function Neural Network and Feedforward Active Disturbance Rejection Control of Permanent Magnet Synchronous Motor

A composite control strategy is proposed to improve the position-tracking performance and anti-interference capabilities of permanent magnet synchronous motors (PMSMs). This strategy integrates an active disturbance rejection controller (ADRC) and a radial basis function neural network (RBFNN) with feedforward control. Initially, the flexibility and robustness of the ADRC are utilized in the position loop control. Subsequently, the parameters of the extended state observer (ESO) within the ADRC are optimized, benefiting from the fast convergence speed and optimal approximation provided by the RBFNN. To further enhance the dynamic tracking performance, a differential feedforward link is introduced between the desired speed and the output signal. The simulation and experimental results demonstrate that when the expected electrical angle inputs are sinusoidal and pulse signals, the incorporation of the feedforward link and the adjustment of parameters in the ADRC lead to improved position-tracking capabilities and greater adaptability to load disturbances.

Pathologic Substrates of Structural Brain Network Resilience and Topology in Parkinson Disease Decedents.

In Parkinson disease (PD), α-synuclein spreading through connected brain regions leads to neuronal loss and brain network disruptions. With diffusion-weighted imaging (DWI), it is possible to capture conventional measures of brain network organization and more advanced measures of brain network resilience. We aimed to investigate which neuropathologic processes contribute to regional network topologic changes and brain network resilience in PD. Using a combined postmortem MRI and histopathology approach, PD and control brain donors with available postmortem in situ 3D T1-weighted MRI, DWI, and brain tissue were selected from the Netherlands Brain Bank and Normal Aging Brain Collection Amsterdam. Probabilistic tractography was performed, and conventional network topologic measures of regional eigenvector centrality and clustering coefficient, and brain network resilience (change in global efficiency upon regional node failure) were calculated. PSer129 α-synuclein, phosphorylated-tau, β-amyloid, neurofilament light-chain immunoreactivity, and synaptophysin density were quantified in 8 cortical regions. Group differences and correlations were assessed with rank-based nonparametric tests, with age, sex, and postmortem delay as covariates. Nineteen clinically defined and pathology-confirmed PD (7 F/12 M, 81 ± 7 years) and 15 control (8 F/7 M, 73 ± 9 years) donors were included. With regional conventional measures, we found lower eigenvector centrality only in the parahippocampal gyrus in PD (d = -1.08, 95% CI 0.003-0.010, p = 0.021), which did not associate with underlying pathology. No differences were found in regional clustering coefficient. With the more advanced measure of brain network resilience, we found that the PD brain network was less resilient to node failure of the dorsal anterior insula compared with the control brain network (d = -1.00, 95% CI 0.0012-0.0015, p = 0.018). This change was not directly driven by neuropathologic processes within the dorsal anterior insula or in connected regions but was associated with higher Braak α-synuclein staging (rs = -0.40, p = 0.036). Although our cohort might suffer from selection bias, our results highlight that regional network disturbances are more complex to interpret than previously believed. Regional neuropathologic processes did not drive regional topologic changes, but a global increase in α-synuclein pathology had a widespread effect on brain network reorganization in PD.

A rapid and reliable targeted LC-MS/MS method for quantitative analysis of the Tryptophan-NAD metabolic network disturbances in tissues and blood of sleep deprivation mice

BackgroundTRY-NAD metabolic network includes TRY (tryptophan), 5-HT (5-hydroxytryptamine), KYN (kynurenine), and NAD (nicotinamide adenine dinucleotide) pathway, which plays a significant role in neurological diseases and ageing. It is important to monitor these metabolites for studying the pathological anatomy of disease and treatment of responses evaluation. Although previous studies have reported quantitative methods for several metabolites in the network, the bottlenecks of simultaneously quantifying the whole metabolic network are their similar structures, diverse physico-chemical properties, and instability. Standardized protocols for the whole metabolic network are still missing, which hinders the in-depth study of TRY-NAD metabolic network in laboratory research and clinical screening. ResultsWe developed a LC-MS/MS method for quantifying 28 metabolites in the TRY-NAD network simultaneously. Optimization was done for the mass spectral parameters, chromatographic conditions and sample pretreatment process. The developed method was fully validated in terms of standard curves, sensitivity, carryover, recovery, matrix effect, accuracy, precision, and stability. The pretreatment of 30 samples only takes 90 min, and the LC-MS/MS running time of one sample is only 13 min. With this method, we bring to light the chaos of global TRY-NAD metabolic network in sleep deprivation mice for the first time, including serum, clotted blood cells, hippocampus, cerebral cortex, and liver. NAD pathway levels in brain and blood decreased, whereas the opposite happened in the liver. The 5-HT pathway decreased and the concentration of KYN increased significantly in the brain. The concentration of many metabolites in KYN pathway (NAD+ de novo synthesis pathway) increased in the liver. SignificanceThis method is the first time to determine the metabolites of KYN, 5-HT and NAD pathway at the same time, and it is found that TRY-NAD metabolic network will be disordered after sleep deprivation. This work clarifies the importance of the pH of the extraction solution, the time and temperature control in pretreatment in standardized protocols building, and overcoming the problems of inconsistent sample pretreatment, separation, matrix effect interference and potential metabolite degradation. This method exhibits great prospects in providing more information on metabolic disturbances caused by sleep deprivation as well as neurological diseases and ageing.

Kombucha–Chlorella–Proteinoid Biosynthetic Classifiers of Audio Signals

ABSTRACTThis paper describes the development of a bioinspired composite material capable of audio classification applications. Hydrogel matrices produced by microorganisms combined with synthetic biology elements, allow for the development of adaptable bioelectronics that connect biology and technology in a customized way. In this study, a composite population of kombucha, chlorella, and proteinoids (thermal proteins) is utilized to respond to acoustic signals converted to electrical waveforms. The kombucha zoogleal mats, which are made and populated by over 60 species of yeasts and bacteria, offer a matrix at the micro level that is connected to the photosynthetic microalgae chlorella. Proteinoids formed through thermal condensation exhibit unique patterns of signaling kinetics. This living material has the ability to be electrically stimulated and can process signals in a way feasible for sensory applications. Using English alphabet audio inputs, a systematic analysis demonstrates the capability to differentiate audio waveforms based solely on biological composite responses. The use of spectral analysis allows for the identification of specific spike timing patterns that encode unique characteristics of individual letters. Moreover, network disturbances result in specific changes in output, so validating the ability to adjust waveform classification. The study demonstrates that kombucha–chlorella–proteinoid composites provide a durable and versatile bioelectronic platform for immediate auditory processing. The work represents progress toward the development of bioelectronic systems that can be customized based on the principles of biological sensory processing, cognition, and adaptation.

Enhanced three-dimensional trajectory tracking control for AUVs in variable operating conditions using FMPC-FTTSMC

In addressing the variable operating conditions encountered in complex underwater tasks, an enhanced three-dimensional tracking control method is proposed for autonomous underwater vehicle (AUV) based on fuzzy model predictive control-finite time terminal sliding mode control (FMPC-FTTSMC). Firstly, to enhance adaptability to tasks and environments, fuzzy model predictive control method is designed to achieve precise three-dimensional trajectory tracking. Additionally, a fuzzy weight allocator is employed to enable AUV to autonomously adjust optimization strategies based on real-time states such as task type, speed, and distance from the seabed, thus better coping with changing conditions and improving the universality of the control method. Secondly, a dynamic controller is designed using the finite-time terminal sliding mode control method, combined with a finite-time radial basis function neural network (FTRBFNN) disturbance observer to accurately estimate disturbances. Finally, simulation results demonstrate that the proposed method effectively reduces optimization solving time compared to traditional model predictive control, achieves trajectory tracking control under various conditions, meets preset state constraints, and demonstrates excellent tracking accuracy and robustness.

Perancangan Sistem Informasi Ticketing Jaringan Pada Dinas Komunikasi, Informatika dan Statistik Provinsi DKI Jakarta

The rapid development of technology affects all aspects of life, especially information systems. With the development of this technology, speed and accuracy are needed to send information so that it can be received by the recipient. For the process of sending data, a communication network is needed as a means of transportation. In the process, it doesn't always go well; sometimes network interference problems occur. To overcome this problem, a system is needed that can record network disturbances so that problems can be resolved and analyzed immediately. The process of recording network disturbances at the DKI Jakarta Province Communication, Informatics, and Statistics Office is currently still using conventional or manual methods, which often creates problems. So it is necessary to have a system that can record these disturbances. The design of this system is made to make it easier for companies to record network disturbances and facilitate analysis if there are repeated disturbances. By using the waterfall method, existing problems can be examined so that the designed system can perform as expected. So that this system makes it easier for network managers to document reported network disturbance data, which results in not requiring a long time to search for or collect manual records. Therefore, this system will make the work system more effective and efficient in terms of time, effort, and cost.Keywords : network, ticketing, waterfall

Robust dynamic train regulation integrated with stop-skipping strategy in urban rail networks: An outer approximation based solution method

In dense urban rail networks with high passenger demands, uncertain disturbances occur frequently, and the resulting train delays will likely spread over the whole network rapidly, hence degrading the service quality offered to passengers. To cope with the uncertainties of frequent disturbances in urban rail networks, this paper proposes a robust train regulation strategy based on the information gap decision theory, which allows the operators to adjust the conservativeness of adjustment schemes flexibly by varying system performances but without the need for prior knowledge of uncertain disturbances. Specifically, considering the coupling relationship between train dynamic flows and passenger dynamic flows, a mixed integer quadratically constrained programming (MIQCP) model is constructed for the robust train regulation problem to generate solutions with immunity against disturbance uncertainties, in which the envelope-bound model is used to characterizing the uncertain sets of disturbances. To meet the real-time requirements of train operation adjustment, a tailored outer approximation algorithm incorporating a two-phase heuristics method is devised to effectively solve the developed robust train regulation model, thereby quickly generating high-quality solutions. Moreover, the warm start technique and domain reduction technique are carefully developed to accelerate the solving procedure. Numerical experiments based on the Beijing metro network illustrate the robustness of the proposed train regulation strategies and the effectiveness of the designed solution approach.

Analysis of power quality and additional loss in distribution network with distributed generation

As the penetration of distributed generation in the power grid, the power quality issues in the active distribution network are becoming increasingly diversified, hence followed with complex changes of the additional loss. The article investigates the additional loss caused by disorder power flow and power quality disturbances in the active distribution network. Firstly, the active distribution network model is established based on typical parameters. The impact of the access of distributed generation on power quality issues, including three-phase imbalance, harmonic distortion and voltage deviation and rise, is analyzed. Then, the influence of different power quality disturbances on additional loss with distributed generation is quantitatively explored. By introducing the concept of harmonic three-phase imbalance, the article analyzes the variation characteristics of network loss under the disturbances of composite power quality issues, and introduces the concept of a loss reduction interval, whose size is related to the average current. Finally, the validity of the analysis is verified through simulation. This article provides a new approach for loss analysis in distribution network containing distributed generation, which is beneficial for identifying and minimizing network loss in distribution networks.