Power Distribution System Research Articles

A REVIEW ON HYBRID ELECTRIC VEHICLES USING VARIOUS ENERGY STORAGE: A MODERN APPLICATION OF RENEWABLE ENERGY SOURCES

Climate change, diminishing reserves of fossil fuels, and energy security demand alternatives to our current course of energy usage and consumption. Energy storage technology has become an enabling technology for renewable energy applications and enhancing power quality in the transmission and distribution power systems. There are various types of energy storage technology are present nowadays, including compressed air energy storage(CAES), flywheel energy storage(FES), pumped hydro energy storage(PHES), battery energy storage(BES), flow battery energy storage(FBES), super conducting magnetic energy storage(SMES), super capacitor energy storage(SCES), hydrogen energy storage, synthetic fuels, and thermal energy storage(TES) and today’s one of the most talked technology using these Energy storage system is Electric Vehicles. Electric vehicles are superior to internal combustion engines (ICE) in efficiency, endurance, durability, acceleration capability and simplicity. Also, they can recover some energy during regenerative braking and they are also friendly with the environment. There are now four types of electric cars: battery electric vehicles (BEVs), plug-in hybrid electric vehicles (PHEVs), conventional hybrid electric vehicles (HEVs), and fuel-cell electric vehicles (FCEVs). And there are three charging methods for electric vehicles, namely conductive, inductive, and battery exchange. This paper will show a detail study of different energy storage systems’ characteristics with their suitability as a storage unit of Electric vehicle.

Optimal loop power flow control of power distribution system using advanced meta-heuristic algorithms

Optimal loop power flow control of power distribution system using advanced meta-heuristic algorithms

Coordinated recovery of interdependent power and water distribution systems

Coordinated recovery of interdependent power and water distribution systems

Enhancing multivariate, multi-step residential load forecasting with spatiotemporal graph attention-enabled transformer

Short-term residential load forecasting (STRLF) holds great significance for the stable and economic operation of distributed power systems. Different households in the same region may exhibit similar consumption patterns owing to the analogous environmental parameters. Incorporating the spatiotemporal correlations can enhance the load forecasting performance of individual households. To this end, a spatiotemporal graph attention (STGA)-enabled Transformer is proposed for multivariate, multi-step residential load forecasting in this paper. Specifically, the multiple residential loads are cast to a graph and a Transformer with a graph sequence-to-sequence (Seq2Seq) structure is employed to model the multi-step load forecasting problem. Gated fusion-based STGA blocks are embedded in the encoder and decoder of the Transformer to extract dynamic spatial correlations and non-linear temporal patterns among multiple residential loads. A transform attention block is further designed to transfer historical graph observations into future graph predictions and alleviate the error propagation between the encoder and decoder. The embedding of multiple attention modules in the Seq2Seq framework allows us to capture the spatiotemporal correlations between residents and achieve confident inference of load values several steps ahead. Numerical simulations on residential data from three different regions demonstrate that the developed Transformer method improves multi-step load forecasting by 14.7% at least, compared to the state-of-the-art benchmarks.

A customised artificial neural network for power distribution system fault detection

AbstractMachine learned fault detection approaches are being increasingly used for fault detection in distribution grid. However, the performance of the models can be improved by customizing the models. In this regard, a customised artificial neural network (CANN) for fault detection in a distribution grid is proposed in this paper. The proposed work develops a CANN that combines the “up‐pyramid” and “down‐pyramid” model of ANN into a “custom‐pyramid” model. As a result, the same model can be used both for determining the types of fault as well as its location. The data needed to train the model has been taken from a reconfigured IEEE‐33 bus distribution system developed in Typhoon HIL real‐time simulator. Spectral‐kurtosis is utilized for extraction of features of the faulted transient signals which are used as input data to develop the CANN. The result showcases that the reduction of input features reduces computational complexity without compromising its accuracy. The proposed model classifies fault location with an accuracy of 95.43%. The proposed method also identifies fault type with an accuracy of 96.08%. Several test cases have been developed to test the method. The method proved to be able to perform in most of the cases.

PV Connected Grid System Using Modified Boost Converter with AI Based Controller

Grid connected solar Photovoltaic (PV) system has a fast growing rate and rapidly becoming an essential part in power distribution systems. Solar energy generated through PV array completely depends on atmospheric conditions, changes in weather affects the amount of output energy, and hence the system needs to be improved according to changes in climate. Thereby, this work focuses on Modified Boost Converter (MBC) with AI based controller for optimizing the performance of grid connected PV module. Moreover, Artificial Neural Network (ANN) based Maximum Power Point Tracking (MPPT) is used in extracting maximum output power from PV system. The single phase Voltage Source Inverter (1 VSI) is utilized in converting the DC supply into AC. For verifying the performance of grid connected PV, it is implemented in MATLAB. The results demonstrates that the proposed converter results with higher voltage gain compared to the other conventional converters.

Series‐arc‐fault diagnosis using feature fusion‐based deep learning model

AbstractThis paper describes the detection of series arc faults, which constitute the major cause of electrical fires, in a power distribution system. Because the characteristics of series arc faults change considerably depending on the load type, their accurate detection and analysis are difficult. We propose a series‐arc‐fault detector that uses a transfer learning (TL)‐based feature fusion model. The model is trained stagewise for various features in the time and frequency domains using a one‐dimensional convolutional neural network combined with a long short‐term memory model that uses an attention mechanism to accurately detect arc‐fault features. To enhance the reliability of the proposed model, we implement an arc‐fault generator compliant with the UL1699 standard and acquire high‐quality data that suitably reflect the real environment. Experimental results show that the proposed model achieves an accuracy of 99.99% in classifying series arc faults for five different loads. Hence, a performance improvement of approximately 1.7% in classification accuracy is reached compared with a feature fusion model that does not incorporate TL‐based model transfer and the attention mechanism.

Voltage Control in Distributed Photovoltaic Power Distribution Networks Using Multi-Agent Deep Reinforcement Learning

Abstract This study introduces a unique method employing the Multi-Agent Deep Deterministic Policy Gradient (MADDPG), a sophisticated deep reinforcement learning algorithm, for efficient voltage control in photovoltaic (PV) power distribution networks. The algorithm’s design emphasizes minimizing the need for communication and effectively managing delays, which are pivotal in ensuring consistent and reliable control in environments with fluctuating renewable energy sources. In simulations using the IEEE-33 power distribution system, the research demonstrates the algorithm’s ability to ensure stable and efficient network functioning, even under varying environmental and load conditions. This highlights its potential as a robust solution for modern and renewable energy-integrated power systems.

Three-phase model of SCIG-based variable speed wind turbine for unbalanced DSLF analysis

Steady state performances of the electric power distribution system are normally assessed or evaluated based on load flow analysis. To properly carry out the analysis, a valid steady state load flow model of each distribution system component, including the wind power plant (WPP), needs to be developed. The present paper proposes a method for modeling and integrating squirrel cage induction generator (SCIG)-based variable speed WPP into a three-phase unbalanced distribution system load flow (DSLF) analysis. The proposed method is based on a single-phase T-circuit model of fixed speed WPP, which has successfully been applied to balanced electric power systems. In the present work, the single-phase T-circuit model is extended and modified to be used in steady state load flow analysis of three-phase unbalanced distribution systems embedded with SCIG-based variable speed WPP. Results of the case studies confirm the validity of the proposed method.

Impedance Specifications for Stability Design of Grid-Connected DC Distribution Power Systems

Impedance Specifications for Stability Design of Grid-Connected DC Distribution Power Systems

Smart meter-based outage detection method for power distribution systems

This paper proposes a new data-driven method for power outage detection. By capturing the changes in data distribution of smart meters (SM), it can detect power outages in partially visible distributed systems. First, a mechanism based on breadth-first search (BFS) is proposed, which decomposes the network into a set of regions to find the location information where power outages are most likely to occur. Then, the SM data for each region, generating a generative adversarial network (GAN), is used in order to extract unsupervised manner implicit temporal behavior under normal conditions. After network training, anomaly scoring technology is used to determine whether the real-time measurement data is the data of a power outage event. Finally, in order to infer the location of a power outage in a multi-area network, a regional coordination process with interdependence be-tween cross-regions is used. At the same time, the concept of entropy is used to provide performance analysis for the algorithm in this paper. This method has been verified on the distribution feeder model with actual SM data. Experimental results show that the algorithm is effective and feasible.

Hybrid Solar PV and WECS Power Quality Improvement by STATCOM

The increasing integration of off-grid energy sources in the utility load has ended up resulting in elevated standards regarding power quality, voltage stabilisation purposes, and efficient energy use. The electrical network Wind and solar energy have been considered to be among the most reliable renewable energy sources. However, the self-sufficient operation of either photovoltaic or wind energy systems does not offer a highly consistent source of electricity production owing to the unpredictability of the wind and solar irradiance availability. As a result of this, a variety of solar and wind power generation systems have the ability to produce a very reliable and promising electrical supply. In the present study, a hybrid wind and photovoltaic panels system model has been provided. This specific type of technology possesses plenty of possibilities for its users from afar. This particular kind of technology is highly beneficial in inaccessible or offshore locations where integrating with the grid is not very cost-effective. Nevertheless, integrating power electronics to dissipated generation (DG) systems brings substantial issues with power quality, which include reactive power adjustment and harmonic development, which throws off the system for power distribution. The present study proposes a simulation framework for a hybrid wind-photovoltaic generation system. The system's efficiency in gridconnected mode is assessed. Calculations of total harmonic distortion (THD) at various speeds of wind were used for assessing the wind-SPV hybrid system's power quality. This hybrid system's power quality has been enhanced owing to its employing of STATCOM.

Enhanced Kepler optimization for efficient penetration of PV sources integrated with STATCOM devices in power distribution systems

This paper introduces an enhanced Fractional Order Kepler Optimizer (FOKO) with a Local Escaping Strategy (LES) to maximize overall energy efficiency in distribution systems, particularly addressing challenges related to the effective penetration of photovoltaic (PV) sources and the emergence of static synchronous compensator (STATCOM) devices. The proposed FOKO retains the positive traits of the standard Kepler Optimizer (KO) while incorporating two additional adjustments to enhance its search performance. Firstly, a fractional order component (FOC) facilitates effective knowledge exchange among agents, preventing premature convergence. Secondly, LES improves the search process by avoiding local optima. The efficacy of FOKO is demonstrated on 28 standard benchmark functions from the CEC 2017 test suite, and its applicability is validated on IEEE 33 and 69-nodes distribution systems. Numerical results confirm the effectiveness of the proposed technique in addressing complex problems such as PV source integration, hourly load variations, and STATCOM deployment, with notable improvements in energy efficiency and system performance. Compared to the initial case regarding the first system, FOKO achieved an average reduction of approximately 62.10 %, while KO, PSO, DE, NNA, MFO, and CO showed reductions of about 61.62 %, 58.15 %, 56.93 %, 60.99 %, 60.26 %, and 53.51 %, respectively. Additionally, for the second system, FOKO achieved a reduction of approximately 65.51 %, while KO, PSO, DE, NNA, MFO, and CO showed reductions of about 65.44 %, 63.56 %, 65.30 %, 62.24 %, 62.91 %, and 60.25 %, respectively. These results underscore the significant improvements in energy efficiency across all algorithms, with FOKO and KO demonstrating particularly promising performance.

SISTEM KETIDAK SEIMBANGAN BEBAN PADA JARINGAN TEGANGAN RENDAH MENGGUNAKAN ALAT PHB – SR ( PERALATAN HUBUNG BAGI SAMBUNGAN RUMAH) DI PT. PLN PERSERO ULP BINJAI TIMUR

Load imbalance in an electric power distribution system often occurs in the field. This is a single-phase load on Low Voltage Network customers that is not the same due to the large number of additional electrical loads that do not pay attention to system load imbalances. As a result of this imbalance, a current appears in the transformer neutral. The current flowing in the neutral of the transformer causes losses, namely losses due to neutral current in the neutral conductor of the transformer and losses due to neutral current flowing to the ground. By realizing PHB SR (Home Connection Equipment) is able to balance the load on the Low Voltage Network. balance the load on the Low Voltage Network periodically and continuously so as to reduce technical shrinkage (losses), simplify the Connection Field in the technical connection of the Low Voltage Network. Connection in the technical connection of the House Connection (SR) on the pole, improve the aesthetics and neatness of the Network System.

RSDA: A Reliable and Secure Data Aggregation Scheme for Smart Grid

Smart grids are modern power distribution systems that utilize advanced technologies to optimize energy generation, distribution, and consumption. The huge amount of data generated by various sources, such as smart meters, sensors, and other devices, can be aggregated for efficient energy management, supporting demand response etc. However, this data can contain sensitive information about individual’s energy usage patterns, behaviour, and preferences, which raises several privacy concerns. Data reliability is another major issue, as the data send from the smart meters to the aggregators may get corrupted due to errors introduced by the communication channel. In this paper, a private-key additive homomorphic encryption scheme based on polar codes is proposed for privacy preserving data aggregation. The proposed scheme not only ensures data confidentiality but also provide data reliability. The security of this scheme relies on the difficulty in decoding the codeword and is achieved by embedding security in the generator matrix and error vector. Security analysis shows that the proposed scheme is resistant against all known attacks against private-key code-based cryptosystems. The performance analysis is evaluated in terms of error performance, communication overhead and computational complexity. The performance comparison with related additive homomorphic encryption scheme shows the efficiency of the proposed scheme.

Power distribution system resilience to extreme dust storms: probabilistic impact assessment and hardening planning

ABSTRACT Extreme dust storms (EDSs) are high-impact low-probability natural disasters, and their occurrence in humid climates can damage the power distribution systems (PDSs) as a critical infrastructure. In this paper, proposed a bi-level stochastic framework for simultaneously hardening substations and distribution lines. In the first level, total capital cost is addressed for PDS hardening under the financial constraints, while in the second level, the expected operating costs are minimized in the case of an EDS under the operating constraints. In the proposed model, the location of remote-controled switches (RCSs) is determined based on the PDS hardening planning results, and the decisions at each level depend on the planning results of the other level. The simulation results at different budget levels show that simultaneous hardening planning of distribution lines and substations considering network reconfiguration can not only reduce expected operating costs, but also can reducing total capital cost to PDS resilience enhancement.

Development of portable deterioration diagnosis device for oil filled transformers based on near‐infrared spectroscopic information

AbstractA transformer plays a key role in electric power distribution system; therefore, when the transformer fails, several electrical components may get affected. As a result, diagnosing the deterioration of transformers is important for the management of electric power distribution system. The furfural content in the insulating oil of a transformer is widely used as an indicator of deterioration because furfural is generated by the decomposition of insulating paper. However, analyzing the furfural content is time consuming because of the requirement of a professional and the complexity of the diagnosis process. In this study, we propose a novel method for analyzing the amount of furfural in the insulating oil based on near‐infrared spectroscopic data obtained using the developed portable spectrometer, which can be used for the on‐site analysis at transformer installation sites. We selected wavenumbers with high correlation coefficients between the furfural content of oil and the spectroscopic data and estimated the furfural content by partial least squares regression. The results suggest that the furfural content can be analyzed in a few minutes using a small amount of oil through a portable diagnostic device.

Transactive Energy System Concept in Power Distribution System- A Review

Transactive Energy System Concept in Power Distribution System- A Review

Real Time Wind Based Power Approximation Model for Improved Performance in Power Distribution Systems

Real Time Wind Based Power Approximation Model for Improved Performance in Power Distribution Systems

Analysis of Load Imbalance in Tarakan Distribution Transformer of PT PLN (Persero) Sukarami Customer Service Unit

Load imbalance in an electric power distribution system always occurs and the cause of this imbalance is unbalanced single-phase loads on low voltage network customers. As a result of this load imbalance, a current arises in the neutral of the transformer, which can cause power losses. The aim of the research is to analyse the load imbalance of the Distribution Transformer PC0044, PC0074, PC0088, PC0129, PC1319 on the Tarakan Feeder of PT PLN (Persero) Sukarami Customer Service Unit. The results of the research show that the highest peak load percentage occurs on the PC1319 Distribution Transformer, namely 93.0874%, and based on PLN ED Decree No. 0017.E/DIR/2014, transformer loading. The largest percentage of load imbalance occurs in the PC0044 Distribution Transformer, namely 38.0952%, and based on PLN ED Decree No. 0017.E/DIR/2014, the current imbalance between phases is included in the bad category because the load imbalance is ≥ 25%. The largest percentage of power losses due to neutral current in the transformer neutral conductor occurs in the PC0044 Distribution Transformer, namely 2.6737%, and based on PLN ED Decree No. 0017.E/DIR/2014, the neutral current TR is included in the good category because the power losses due to the neutral current in the transformer neutral conductor are <10%. The largest percentage of power losses due to neutral current flowing to the ground occurs in the PC0044 Distribution Transformer, namely 5.0784%.