Low Voltage Distribution System Research Articles

Arc-1DCNN: An enhanced model for series arc fault detection

Series Arc Faults (SAFs) represent a prominent cause of electrical fires in low-voltage distribution systems, often arising from faulty connections or deteriorated insulation. These SAFs occurrences generate high-temperature arcs that endanger electrical system safety. As a result, detecting and accurately identifying SAFs have become crucial concerns. However, the line noise interference and the complexity of the electrical environment make SAFs detection challenging. In this paper, we propose a novel method that combines Wiener filtering with the Arc-1DCNN model to enhance SAFs detection. The method leverages Wiener filtering to enhance current signal, effectively reducing noise interference and providing a more robust dataset for training. To fully exploit the rich high-frequency characteristics of SAFs, Arc-1DCNN incorporates a High-Frequency-Feature-Attention module, enabling the model to capture subtle SAFs anomalies and significantly improving detection accuracy. Experimental validation demonstrates Arc-1DCNN's exceptional performance with 99.94% detection accuracy for SAFs, showcasing its potential for addressing SAFs detection challenges.

Various Feature-Based Series Direct Current Arc Fault Detection Methods using Intelligence Learning Models and Diverse Domain Exclusion Techniques

The expansion of DC electrical distribution systems necessitates advancements in detecting and mitigating DC arc events, a significant contributor to fire accidents in low-voltage DC distribution systems. Detecting DC arc faults poses considerable challenges, making them a major safety concern in DC power lines. Conventional approaches mainly rely on arc current, which can vary during normal operation, potentially leading to false alarms. Moreover, these methods often require manual adjustment of detection thresholds for different systems, introducing the risk of malfunction. This study proposes an advanced arc fault recognition procedure that extracts and utilizes various key features for the DC arc detection. This work investigated new various features, which are the square average, the average, the median, the rms, the peak-to-peak, and the variance values, to find out which one can be the most effective features to detect the DC arc failure. The results of this detection process show good evidence for the effectiveness and reliability of the proposed malfunction detecting plan.

A New Approach for Single-Phase-to-Ground Fault Detection in Mid-Voltage Systems Using Low-Voltage Sensors

Single-phase-to-ground fault in low-current grounding systems represents a serious public safety concern. Low-voltage (LV) sensors, with their growing maturity, can now monitor multiple points of the mid-voltage (MV) distribution network. This paper proposes a new method for identifying single-phase-to-ground line faults and locating them using LV sensors deployed on the LV side of distribution transformers. We analyze the characteristics of the negative-sequence signal on the LV side after a single-phase grounding fault occurs on the MV side. The negative-sequence current can distinguish between fault and non-fault lines. By setting the ratio coefficient of negative-sequence voltage and positive-sequence voltage, we can use multi-point collaborative calculation and comparison to determine the section of the fault point. We consider the unbalanced load on the LV side and the special case of a fault point on one end of the line. Through simulation of combined MV and LV distribution systems in MATLAB software and dynamic model experiments, we verify that the proposed method has good robustness and accuracy. Monitoring the status information of the MV distribution network through LV sensors has great potential in practical application and implementation for realizing the fault detection of low-current grounding systems.

Design of Instrumentation and Control Components of Power Distribution Systems

Introduction. In recent years, the development of high-voltage power systems has received a boost due to the need for infrastructural support for priority development areas. Universal models and algorithms are required to implement processes in power components and identify their optimal parameters. However, there are no such solutions. Accordingly, there are no ready-made subsystems with control and optimization algorithms adequate to the tasks under consideration. The objective of the presented research is to develop an optimization subsystem for the design of control and measurement components of power distribution systems.Materials and Methods. Methods for constructing automated design systems, optimization, system analysis, mathematical modeling, and adaptive control were used. When selecting methods, we proceeded from the fact that the components of power distribution systems consisted of a finite number of elements. The synthesis of a power system includes tens or hundreds of sequential operations. This was taken into account in the developed models and algorithms.Results. The possibilities of managing and monitoring manufacturing processes (MP) for the production of components of low-voltage power distribution systems were shown in terms of checking the operability and correct functioning of processing equipment. A modular structure was created to allow the integration of CAD output files into the manufacturing processes of energy distribution system components. A functional diagram of a subsystem for control and monitoring of the manufacturing processes of the production of components of power distribution systems was developed. The proposed schematic diagram of production control showed how the data collection subsystem, management system, and operating mechanisms were involved in the control of operations. The multi-level optimization module model created within the framework of this research sequentially optimized the service intensity of the i-th block, the input flow separation coefficients, and the priorities of the original data flows that form the input flow of the i-th block.Discussion and Conclusion. The combined application of modeling, system analysis, and optimization methods maintains control of the accuracy of the generated power components. The algorithm for controlling electrical loads opens up opportunities for creating a mathematical model of a power supply system that combines management, control, and monitoring, which ultimately leads to an improvement in the quality of electric power. The solution can be in demand in the development of power systems of priority development areas.

Conditional scenario-based energy management algorithm with uncertain correlated forecasts

This paper introduces the application of the novel approach known as Conditional Scenario-Based Model Predictive Control (CSB-MPC) into energy management in a low voltage distribution system (LVDS). The LVDS comprises renewable energy sources (RES) and an energy storage system, with correlated power generation and demand under uncertainty. This correlation is leveraged to derive a reduced set of conditional scenarios and their associated probabilities. This reduced set retains the essential characteristics of a larger set of equiprobable scenarios generated based on information about uncertainties in power generation and demand forecasts. Instead of computationally demanding methods using the larger set, a feasible Scenario-Based Mixed-Integer Linear Program (MILP) is solved by employing the reduced set. This optimisation minimises costs associated with energy consumption from the main grid and the potential waste of generated power. In the objective function, the scenarios are weighted by their probabilities to mitigate the impact of less likely forecasts, leading to efficient LVDS component utilisation and reduced operating costs. Simulation results comparing the performance of the LVDS with CSB-MPC against other MPC approaches, including classic scenario-based, chance-constrained, and deterministic methods, reveal that CSB-MPC consistently exhibits higher probabilities of operational constraint satisfaction, with differences of up to 10% or even 30%. Notably, CSB-MPC achieves these results with similar solution times to classic scenario-based MPC but with lower LVDS operating costs, between 6.62% to 11.31% less.

Low-Voltage ac and Low-Voltage dc Distribution Systems: An Assessment of Losses and Power Capacity considering the Penetration of Renewable Energy Sources

Low-Voltage ac and Low-Voltage dc Distribution Systems: An Assessment of Losses and Power Capacity considering the Penetration of Renewable Energy Sources

A Transformerless Converter with Common-Mode Decoupling in Low-Voltage Hybrid Grids

Compared with isolated converters, transformerless converters are a preferred choice in low-voltage grids due to their efficiency and lower cost. However, leakage current and common mode (CM) voltage appear through the converter and ground in hybrid grids, which consist of AC and DC subgrids. The leakage current and CM voltage seriously influence operation and power quality in low-voltage distribution systems. This paper proposes a common-ground-type (CGT) converter equipped with a CM decoupling control strategy to eliminate the leakage current and CM voltage. A CM model is derived, and the leakage current and CM voltage are analyzed in detail. A CGT four-leg converter is constructed to eliminate the high frequency CM voltage. A dual DQ current control loop is developed to suppress the DC double-frequency ripple. Additionally, an active damping method is proposed, based on the neutral current feed-forward plus inductor current feedback, to attenuate the low frequency CM voltage. The proposed converter and control strategy guarantees excellent performance in suppressing leakage current and CM voltage. The DC voltage of the converter connected to the DC grid maintains stability and symmetry. The leakage current is significantly reduced, and the leakage current suppression performance is improved by 83%. The high frequency CM voltage is attenuated from 50%udc to 2%udc, and the low frequency CM voltage is suppressed from approximately 32%udc to 3%udc, which is a significant improvement compared with the traditional method. In addition, the proposed control strategy has good transient performance when the load changes abruptly. Finally, an experimental platform is established to validate the feasibility and performance. The experiment results showed that the proposed control strategy improves the system performance and power quality.

Multi-Branch Line Fault Arc Detection Method Based on the Improved Northern Goshawk Optimization Adaptive Base Class LogitBoost Algorithm

In low-voltage AC distribution systems, when a series arc fault occurs in a branch with multiple loads operating in parallel, it will be significantly more difficult to identify. Existing arc fault detection methods make it difficult to effectively detect faults occurring in the lower-level branch. This study introduces a novel series arc fault detection approach based on the improved northern goshawk optimization adaptive base class LogitBoost (INGO-ABCLogitBoost) algorithm. Considering the zero-rest, intermittent, and random fluctuation and high-frequency features of the arc current, the zero-rest coefficient, discrete coefficient, harmonic amplitude, and wavelet entropy are proposed to establish the high-dimensional feature matrix of the arc current. The ReliefF feature selection algorithm is used to optimize feature quality and decrease feature dimensionality. Subsequently, the ABCLogitBoost fault detection model is proposed, with the INGO algorithm applied to optimize the model parameters, thus enhancing the model’s diagnostic capabilities. The efficacy of the proposed diagnostic model is validated through the construction of a multi-load arc simulation system. The simulation results show that the overall fault diagnosis accuracy of the proposed method reaches 99.01% and can effectively identify the fault load types, which helps to locate the fault location.

A Method to Compute Frequency Domain Models Based on Direction-Dependent Derivative of the Variation of Voltage and Current Phasors

Harmonic pollution and other potential problems are increasing due to the rising demand for Power Electronic devices in low-voltage distribution systems. The adequate modeling of the harmonic characteristics of these devices is essential for assessing their impact. Frequency Domain Models (FDMs) are based on either matrix or tensor definitions, and different methods have been proposed to compute those models' parameters. This paper proposes a method to compute matrix or tensor-based FDMs by exploiting the nonholomorphic properties of complex functions. Thus, the direction-dependent derivative of the variation of voltage-current phasors is optimized over measurements set. By proving that tensor and matrix-based models are equivalent, it is guaranteed that the method can be applied to compute both models. The Proposed and Tensorial Parametrization methods are compared using the power of the error signal as a percentage of the power of the current signal achieving a one-digit reduction in a proportion of 4:1.

Three-phase series arc fault detection based on two-dimensional attention PoolFormer

Series arc faults are a major causation of electrical fires. The complexity of load types in low-voltage distribution systems makes arc faults detection more challenging for three-phase circuits with inverters. To solve this problem, this paper proposes a detection method based on two-dimensional attention PoolFormer. Firstly, a low-voltage three-phase series arc faults data acquisition platform is built to collect the required data. The collected current signals are encoded as pictures through image mapping and projected into a more discriminative space, while increasing the magnitude of the dataset. Subsequently, the two-dimensional attention PoolFormer algorithm model is constructed to fully exploit the feature information between different fault categories. This model has multi-scale parallel convolution to extract features of input samples and perform information fusion. Considering also the ability to seize the location characteristics of fault information well, the two-dimensional attention is designed to be added inside the algorithm, to grasp the precise location information to enhance the performance of the algorithm. Finally, the dataset is fed into the two-dimensional attention PoolFormer model for training and testing. The results show that the accuracy of the method proposed in this paper can achieve 99.36%.

Transformer-Customer Connectivity Relationship Identification for Low-Voltage Distribution System With High Penetration of Household PV Systems

Transformer-Customer Connectivity Relationship Identification for Low-Voltage Distribution System With High Penetration of Household PV Systems

Automated fault location scheme for low voltage smart distribution systems

AbstractIntegrating the self‐healing capability realizes the automated protection of smart distribution systems. This article presents a fault location scheme for low voltage (LV) distribution systems based on the information collected from the smart meters. First, a fault condition is detected and classified by processing the current signal at the secondary side of the distribution transformer by an intelligent electronic device. Then, the faulty feeder and section are determined by calculating the fault‐imposed component of nodal voltages. The reliable performance of the proposed scheme is verified through several case studies using a real semi‐rural distribution system.

Assessment of fault potential and touch voltage in the low-voltage distribution line system of the residential community

Considering the increasing risk of electric shock in old residential communities, this paper investigated fault potential and consequential human-body touch voltage in the low-voltage (LV) distribution network under typical line-fault conditions by numerical simulation with a circuit approach. Three protective grounding configurations of the LV distribution system were of concern, and the electric shock scenarios considering practical situations were introduced for assessment. It has been observed that the phase-to-ground fault potential of equipment increases when the fault location is closer to the faulty equipment itself and decreases when it moves further toward the circuit end. The equipment in a building is likely exposed to the dangerous phase-to-ground fault potential under the line fault. As for line-to-neutral short circuit and neutral-line-open fault, the fault potential distribution mainly depends on the specific grounding configurations. Bonding of non-exposed steelwork in the buried building foundation to the nearby grounded grid may not be recommended in the literature, but it could effectively mitigate the touch voltage in and around the building of concern in this paper. Note that this measure, together with the local equipotential bonding method inside a flat, can provide enhanced protection against electric shock even if the protective device fails to operate or is unavailable. Among three grounding configurations, the one with multiple grounding points of the neutral wires up to the entrance of a building leads to the least dangerous touch voltage cases.

IoT-based compensation and control of power theft in weak distribution system

This paper presents a prototype model of real-time rampant power theft detection and compensating the required power current components through switching of distribution static compensator (DSTATCOM) using IoT (Internet of Things) based microcontroller via the GSM module. In this case study, an Internet of Things (IoT) application is used to wirelessly transmit data of the difference of smart meters through intelligent current sensors with ESP8266 module. Transmitted pulse sequences find out the derivatives of functions at discrete points through weight function. A weighting function includes power theft detection information in the pulse sequence series form. It computes the unknown sample of pulse in the line that identified the power theft and the quality of the power supply. The sequence error of the of energy meter units communicates microcontroller through the IoT. Microcontroller communicate to μ-PMU to addressed power quality error with a GPS receiver in the line. Based on weight function an algorithm has been developed for switching DSTATCOM for compensation of power quality. The reference and feedback quantities’ resultant errors are sampled within a time rate of the weight function. This control approach is applied with dynamic load to analyze the power quality issues. The results are implemented in MATLAB Simulink and validated experimentally for power theft detection and its compensation in low voltage distribution system.

Enhancing anomaly detection accuracy and interpretability in low-quality and class imbalanced data: A comprehensive approach

With the rapid advancements in smart meters, energy internet, and high-performance computing technologies, deep learning methods are increasingly used for detecting anomalies in building electricity consumption. However, these models grapple with challenges like outliers, missing data, and imbalanced categories. Moreover, these models are often considered as black boxes due to their lack of interpretability. This limitation impedes the practical deployment of deep learning methods. Focusing on these issues, this study presents an anomaly detection method aimed at improving anomaly detection accuracy and interpretability in low-quality and class imbalanced data. Initially, our method established data preprocessing models to handle with outliers and missing data. Subsequently, an unbalance elimination model, named GRU-MACGANS, was designed to augment the minority category data. The SA-CNN-BiGRU-FCN model, incorporating a knowledge transfer strategy, was implemented to identify anomalous behavior. Finally, the results of SA-CNN-BiGRU-FCN model were interpreted based on the contrastive explanations method. Our case study of a low-voltage distribution system demonstrates the effectiveness of our approach. Data preprocessing models and GRU-MACGANS enhance data quality and alleviate category imbalance issues in anomaly detection. The interpretable analysis provides insights into the ability of detection models to identify representative features of different electricity consumption behaviors for effective anomaly detection.

Enhancing Performance of Andronov-Hopf Oscillator-Based Grid-Forming Converters in Microgrids With Non-Invasive Online Impedance Estimation

The Andronov-Hopf based Virtual Oscillator Control (AHO) is an emerging control strategy for grid-forming converters (GFC) to ensure the stability of the future power system with high level of penetration of renewable energies. AHO allows converters to operate in either grid-connected and islanded operating modes. However, the application of this control strategy is limited due to the inherent problem in simultaneous controlling active and reactive power output of GFCs. This paper presents a modification in AHO control structure, and adopts a wide-band grid impedance estimation to improve the dispatchability of AHO in grid-connected mode. The impedance estimation algorithm is embedded into the control loop of AHO, and requires only the measurement of the voltages and currents at the point of common coupling. Thus it is cost-effective and practical. The performance of the proposed method is validated in both simplestructure, and large low-voltage benchmark distribution systems, using MATLAB/Simulink software and the real-time Opal-RT platform. The simulation results confirm the high-accuracy of the estimation and the capability of AHO in simultaneously controlling active and reactive power with zero steady-state errors. Additionally, this paper discusses the small-signal stability and Lyapunov large signal stability of the proposed AHO and compares it to existing methods.

Reliability Comparison of Rural & Urban LVDS Networks with Corresponding HVDS Networks Including Substation Reliability

This paper mainly determines the comparison of reliability of an existing rural area & urban area LVDS (low voltage distribution system) with their respective proposed high voltage distribution system (HVDS) and also including their 33/11 kV Substation. Ring main substation and single bus bar substation configuration are considered for urban and rural distribution systems respectively. The reliability of ring bus substation used in urban area is more as compared to single bus substation used in rural area. The load point reliability indices are calculated considering both substation reliability indices and radial feeder indices. The substation failure rate is calculated by considering overlapping outages approach and whereas the radial feeder indices are calculated by using cut set approach. Further, the LVDS Network is converted into the HVDS Network by replacing a high rating distribution transformer (DTR) supplying to a group of customers with Large in number with multiple small rating distribution transformers near to consumer terminals with less number of customers in each group. This conversion leads to Minimum length of LT Lines in HVDS Which results in improving voltage profile, reliability and efficiency. In this paper the disconnecting switches and alternative supply are also considered on both LVDS and HVDS Networks in the calculation of the reliability of to assess the reliability of the radial distribution system (FMEA)Failure mode effect analysis is used. This study uses a LVDS and HVDS system, and uses an alternative power source and disconnectors, to determine reliability indices of the rural area network and urban area network distribution system. In this paper the important distribution reliability indices to reduce system average interruption durations (SAIDI), Energy Not Supplied (ENS) and ASAI for LVDS and corresponding HVDS are calculated. The research idea is proposed on a Indian practical networks.

Research on Dynamic and Thermal Effects Based on the Calculation of the Short-Circuit Current in Low-Voltage DC Distribution Systems for Civil Buildings

The verification of short-circuit effects is very important for ensuring the safety of equipment and power systems. Compared with that in alternating current (AC) systems, research on this issue in direct current (DC) systems is scarce, and it is urgently necessary to develop an accurate verification method for applications in DC systems. This research establishes an equivalent model of a pole–pole cable short-circuit according to the characteristics of low-voltage DC distribution systems in civil buildings. Through theoretical analysis and numerical simulation, the development process of a short circuit is summarized, and the methods of verifying dynamic and thermal effects based on the time-domain characteristics of the short-circuit current are specified. By calculating the peak value and Joule integral of the short-circuit current, in comparison with those in the IEC 61660 (1997) standard, this research points out that the method in the IEC 61660 (1997) standard is insufficient. Finally, the short-circuit peak current is greatly affected by the DC-link capacitance, the steady-state current is directly related to the filter inductance of the AC-link; and the verification of the thermal effect requires the calculation of the Joule integral in the transient and steady state.

Techno-Economical Effects of Adopting Small Capacity Transformers on Ado-Ekiti 11/0.415 kV Power Distribution Networks

The major portion power loss in power system occurs in the components of low voltage (LV) networks. Low voltage distribution system (LVDS) configuration has been observed to contribute significantly to power loss and unacceptable voltage drop in the system due to its structure and higher current flow in the network. The adoption of small capacity transformers as high voltage distribution system (HVDS) configuration is increasingly applied to the network for power loss reduction and the improvement of voltage profile. This study aims to evaluate the technical and economic implication of the use of HVDS configuration on Ado-Ekiti 11/0.415 kV power distribution networks and provide necessary recommendations. The network data was obtained and modeled on NEPLAN software for simulation and technical evaluation of the networks. The existing LV lines were upgraded to high voltage (HV) and remodeled with small capacity transformers and comparative analysis of the technical and economic effects of the networks were carried out. The results obtained showed that there could be feeder capacity increase of 92.03 kW and power loss reduction of 347.17 kW and 459.72 kVar following reconfiguration from LVDS to HVDS. The losses in the transformers and lines were significantly reduced by 37.8% for HVDS configuration when compared with the LVDS configuration. The voltage profile was significantly improved with voltage deviations in the range of ± 0.01 to ± 0.03 of bus voltage for the HVDS network when compared to the existing network. The cost of implementation of the HVDS network substation was calculated to be ₦807,238,715.50 and with the combination of annual savings and an increment in tariff of ₦5/kWh in the billing charge of unit energy consumption resulted in a payback period of 2 to 3 years for the estimated capital investment. This study showed that technical performance of HVDS implementation on the Ado-Ekiti network feeder was better than that of the existing network.

Series Arc Fault Detection Based on Multimodal Feature Fusion.

In low-voltage distribution systems, the load types are complex, so traditional detection methods cannot effectively identify series arc faults. To address this problem, this paper proposes an arc fault detection method based on multimodal feature fusion. Firstly, the different mode features of the current signal are extracted by mathematical statistics, Fourier transform, wavelet packet transform, and continuous wavelet transform. The different modal features include one-dimensional features, such as time-domain features, frequency-domain features, and wavelet packet energy features, and two-dimensional features of time-spectrum images. Secondly, the extracted features are preprocessed and prioritized for importance based on different machine learning algorithms to improve the feature data quality. The features of higher importance are input into an arc fault detection model. Finally, an arc fault detection model is constructed based on a one-dimensional convolutional network and a deep residual shrinkage network to achieve high accuracy. The proposed detection method has higher detection accuracy and better performance compared with the arc fault detection method based on single-mode features.