Power System Distribution Network Research Articles

Development of hierarchical two-stage constrained spectral clustering algorithm to enhance power system distribution network resiliency under zonal attacks

Development of hierarchical two-stage constrained spectral clustering algorithm to enhance power system distribution network resiliency under zonal attacks

Optimize single line to ground fault detection in distribution grid power system using artificial bee colony

The most common power system (PS) distribution network fault, single lineto-ground fault (SLGF), causes residual current (I res) to start an electrical arc and high voltage (HV) three times the rated voltage in other healthy phases. HV from capacitive currents (IC) damages cable insulation and PS appliances. Peterson The neutral point coil (PC) reduces (I res) and extinguishes the electric arc, but the fault current (I fault) remains below the protection devices' threshold. Operations and equipment are riskier. PC adaptive eliminates electrical arcs, making the network safer. This paper detects I faults online using Texas instrument validation in MATLAB and adaptive by artificial bee colony (ABC). This paper discusses Texas instrument fault current detection and MATLAB validation. It improves system reliability, device protection, and copper savings by thousands of tons. ABC intelligently optimizes many mathematical problems. ABC with network neural artificial intelligence (AI) improves algorithm performance (artificial bee colony network neural (ABCNN)). This new method may improve distribution network SLGF detection. This first work can work online in electrical power stations by building the (eZdsp F28335-RS232) into the program to send fault signals to the control when SLGF occurs without damaging devices, equipment, cables, or power outages.

Differential equation fault location algorithm with harmonic effects in power system

About 80% of faults in the power system distribution are earth faults. Studies to find effective methods to identify and locate faults in distribution networks are still relevant, in addition to the presence of harmonic signals that distort waves and create deviations in the power system that can cause many problems to the protection relay. This study focuses on a single line-to-ground (SLG) fault location algorithm in a power system distribution network based on fundamental frequency measured using the differential equation method. The developed algorithm considers the presence of harmonics components in the simulation network. In this study, several filters were tested to obtain the lowest fault location error to reduce the effect of harmonic components on the developed fault location algorithm. The network model is simulated using the alternate transients program (ATP)Draw simulation program. Several fault scenarios have been implemented during the simulation, such as fault resistance, fault distance, and fault inception angle. The final results show that the proposed algorithm can estimate the fault distance successfully with an acceptable fault location error. Based on the simulation results, the differential equation continuous wavelet technique (CWT) filter-based algorithm produced an accurate fault location result with a mean average error (MAE) of less than 5%.

A cyber attack detection strategy for plug-in electric vehicles during charging based on CEEMDAN and Broad Learning System

Cyber attacks targeting the charging process of plug-in electric vehicles (PEVs) have become more frequent with the popularity of PEVs, posing a significant threat to the battery life of PEVs and the stability of the power distribution network system. To mitigate this threat, a cyber attack detection strategy based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Broad Learning System (BLS) is proposed in this paper. The strategy exploits the coupling between PEVs and the power distribution network system during the charging process to achieve the detection of cyber attacks. CEEMDAN is implemented to extract features of system signals, and BLS uses the features extracted by CEEMDAN for feature mapping and enhancement to capture the presence of cyber attacks. Simulation analysis demonstrates that the proposed strategy can achieve more than 85% detection accuracy, with CEEMDAN bringing about 7% improvement in accuracy.

Robust Planning Method for Photovoltaic Microgrid Energy Storage Considering Source-Load Flexibility Resources

The microgrid based on distributed generation is one of the new forms of power system distribution network, and energy storage can provide important support for the access of distributed generation. Due to the shortcomings of the traditional photovoltaic microgrid energy storage method, the energy storage capacity is low. To improve the energy storage level of the photovoltaic microgrid, the robustness planning method of photovoltaic microgrid energy storage considering the flexibility resources of source, network and load is studied. Based on the flexibility resources of the microgrid, the balance index of flexibility supply and demand and the planning index of flexibility response capability is proposed. According to the source-network load algorithm, the optimal scheme of resource complementation is obtained. Based on the storage battery and supercapacitor of the photovoltaic microgrid, the photovoltaic microgrid energy storage robust configuration model is constructed. Comparing the energy storage planning method designed in this paper with two groups of traditional methods, the experimental results show that in the same energy storage time, the energy storage capacity of this method accounts for 50.49%, while that of the traditional group 1 and group 2 is 32.52% and 41.26%, respectively. The proposed PV microgrid robust planning method considering source-load flexibility is reasonable and effective in the energy storage resource allocation scheme, which is of great significance for promoting energy transformation and building a new power system.

Optimal joint operation of coupled transportation and power distribution urban networks

The number of Electric Vehicles (EVs) and consequently their penetration level into urban society is increasing which has imperatively reinforced the need for a joint stochastic operational planning of Transportation Network (TN) and Power Distribution Network (PDN). This paper solves a stochastic multi-agent simulation-based model with the objective of minimizing the total cost of interdependent TN and PDN systems. Capturing the temporally dynamic inter-dependencies between the coupled networks, an equilibrium solution results in optimized system cost. In addition, the impact of large-scale EV integration into the PDN is assessed through the mutual coupling of both networks by solving the optimization problems, i.e., optimal EV routing using traffic assignment problem and optimal power flow using branch flow model. Previous works in the area of joint operation of TN and PDN networks fall short in considering the time-varying and dynamic nature of all effective parameters in the coupled TN and PDN system. In this paper, a Dynamic User Equilibrium (DUE) network model is proposed to capture the optimal traffic distribution in TN as well as optimal power flow in PDN. A modified IEEE 30 bus system is adapted to a low voltage power network to examine the EV charging impact on the power grid. Our case study demonstrates the enhanced operation of the joint networks incorporating heterogeneous EV characteristics such as battery State of Charge (SoC), charging requests as well as PDN network’s marginal prices. The results of our simulations show how solving our defined coupled optimization problem reduces the total cost of the defined case study by 36% compared to the baseline scenario. The results also show a 45% improvement on the maximum EV penetration level with only minimal voltage deviation (less than 0.3%).

Improvement of cable current carrying capacity using COMSOL software

Based on the limitation of the characteristics of materials, in the transmission and distribution network of power system, the relationship between cable current carrying capacity and temperature is inseparable. Improving the ampacity of power cables within the allowable range of temperature is important to the power grid. Using COMSOL multi physical field simulation software, the model of cable to calculate the current carrying capacity is established, and the relationship between cable capacity and temperature field is analyzed. On this premise, the effects of cable placement, convective thermal conductivity and different environmental conditions on the temperature field distribution are further analyzed. The simulation results show that increasing the cable spacing, improving the convective thermal conductivity and maintaining a low ambient temperature the current carrying capacity of the cable can be improved.

Machine Learning Approach for Smart Distribution Transformers Load Monitoring and Management System

Distribution transformers are an integral part of the power distribution system network and emerging smart grids. With the increasing dynamic service requirements of consumers, there is a higher likelihood of transformer failures due to overloading, feeder line faults, and ineffective cooling. As a consequence, their general longevity has been diminished, and the maintenance efforts of utility providers prove inadequate in efficiently monitoring and detecting transformer conditions. Existing Supervisory Control and Data Acquisition (SCADA) metering points are sparsely allocated in the network, making fault detection in feeder lines limited. To address these issues, this work proposes an IoT system for real-time distribution transformer load monitoring and anomaly detection. The monitoring system consists of a low-cost IoT gateway and sensor module which collects a three-phase load current profile, and oil levels/temperature from a distributed transformer network, specifically at the feeder side. The data are communicated through the publish/subscribe paradigm to a cloud IoT pipeline and stored in a cloud database after processing. An anomaly detection algorithm in the form of Isolation Forest is implemented to intelligently detect likely faults within a time window of 24 h prior. A mobile application was implemented to interact with the cloud database, visualize the real-time conditions of the transformers, and track them geographically. The proposed work can therefore reduce transformer maintenance costs with real-time monitoring and facilitate predictive fault analysis.

Arcing fault diagnosis using enhanced cross-correlation technique

This study explored the potential use of cross-correlation as a technique for detecting arcing faults in a power system distribution network. The cross-correlation technique was employed to investigate the effect of each antenna placement as a detection device, time difference of arrival (TDOA), time delay, and correlation magnitude of arcing signals detected during on-line arcing fault measurement. The arcing fault was detected using four antennas that had been set up around the arc source point in a high voltage (HV) laboratory. The measurements were taken using a digital oscilloscope. For precise results, the Discrete Wavelet Transform (DWT) denoising technique combined with cross-correlation (CC) technique were applied using MATLAB software to identify the arcing signals detected in order to diagnose the differentiation between noisy and real arcing fault signals. Further assessment was carried out by performing a cross-correlation technique on the real arcing signals obtained to find the similarities and arrival time’s delay between single arcing signals’ placement. The outcome shows that all measurements including the time difference of arrival (TDOA), correlation magnitude, time delay, and antennas’ placement towards the arcing source point are valuable in determining the arcing signals detected precisely.

Big Data Application Technology and Prospect Analysis in Smart Distribution Network

Intelligent power distribution network system main application technology including the communications, computer and network, the modern electronic technology, etc., adapter power automation system main components for the master station, terminal automation monitoring system and communication system, has formed a complete collection, transmission and processing in the integration of information system, to realize the remote management of distribution network. The application of big data technology in smart distribution network makes it more intelligent and ADAPTS to the needs of social development. This paper analyzes the application technology and prospect of smart distribution network big data.

Investigation of Existence of Steady States of a Solar Power Plant Operating in the Distribution Network of an Electric Power System

The paper presents the results of studies of the steady-state operating modes of solar photovoltaic stations. Using the data obtained, it is possible to significantly increase the efficiency of using solar power generating plants and significantly increase the service life of additional equipment. The relevance of this study is due to the increasing demand for low-power electric power systems, as well as for renewable energy technologies. For the research, materials were taken from relevant international departments and agencies, the Ministry of Energy of the Russian Federation. In addition, the sources of data are publications and studies of Russian and foreign scientists on the issues under study. The Federal Law No. 35-FZ “On Electricity”, adopted in December 2019 by the State Duma of the Russian Federation, introduces such a concept as “Micro-generation facility”, thereby simplifying the possibility of installing, connecting to the general network of an electrical system, for example, a small solar power plant (SPP) consumer. Small-scale electric power industry at this stage of its life is an energy-efficient tool in the restructuring of the energy sector of the Russian Federation, contributing to the abandonment of the traditional centralized system based on the use of large sources of electric power production, and the transition to alternative methods of generating electricity based on the use of energy sources, updated for specific natural conditions and requests of potential consumers. Reducing pollutants and emissions that are harmful to human health can reduce the level of the existing burden on the country's economy and give an impetus to the development of its potential due to the freed up resources. reduce the burden on the economy, thereby freeing up resources for its growth. Of course, the transition from traditional economic models to the model of “green growth” can be realized only if significant efforts are made to expand international cooperation in this area. At the same time, there is a need for consistent implementation of events at various levels on the part of all states, as well as maintaining the chosen political course for many years.

Electric Power System Monitoring and Theft Detection using Power Line Communication

Electric power theft is a serious concern in the world irrespective of being major revenue losses and developing a nation. More than one-third part of the electricity generated power is lost due to electric theft, power loss, and inefficiencies in the distribution system. Interdicted or illegal utilization of electricity has not only affected economically but also obstructs the design and modeling phases of the power system. Due to electric theft, providing wrong data input values for power system analysis and difficult to load forecasting. In this paper, an inventive Simulink model is designed to detect and monitoring of electric power theft in power system distribution networks through Power Line Communication (PLC). Electric power theft was detected with variance amendment in the amplitude of carrier signal with a narrow band. PLC technique is utilized for data communication over the power line. A narrow band power line carrier signal which has high frequency transferred in power line alongside with power frequency signal. The deviation in the amplitude of the transmitted carrier signal is monitored at the regular time- intervals and the stealing of electricity can be distinguished by the computing of distinction change within the amplitude of the carrier signal. In a normal case, the signal present fixes pattern and waveform, but in the case of power theft, the signal shows some variation and disturbance in a within waveform pattern. A pattern recognition and monitoring approach is used for direct power theft in the PLC model. The Simulink model is performed on MATLAB software to analyze the performance and efficient results that satisfy the proposed Simulink model.

Investigation of Existence of Steady States of a Solar Power Plant Operating in the Distribution Network of an Electric Power System

The paper presents the results of studies of the steady-state operating modes of solar photovoltaic stations. Using the data obtained, it is possible to significantly increase the efficiency of using solar power generating plants and significantly increase the service life of additional equipment. The relevance of this study is due to the increasing demand for low-power electric power systems, as well as for renewable energy technologies. For the research, materials were taken from relevant international departments and agencies, the Ministry of Energy of the Russian Federation. In addition, the sources of data are publications and studies of Russian and foreign scientists on the issues under study. The Federal Law No. 35-FZ “On Electricity”, adopted in December 2019 by the State Duma of the Russian Federation, introduces such a concept as “Micro-generation facility”, thereby simplifying the possibility of installing, connecting to the general network of an electrical system, for example, a small solar power plant (SPP) consumer. Small-scale electric power industry at this stage of its life is an energy-efficient tool in the restructuring of the energy sector of the Russian Federation, contributing to the abandonment of the traditional centralized system based on the use of large sources of electric power production, and the transition to alternative methods of generating electricity based on the use of energy sources, updated for specific natural conditions and requests of potential consumers. Reducing pollutants and emissions that are harmful to human health can reduce the level of the existing burden on the country's economy and give an impetus to the development of its potential due to the freed up resources. reduce the burden on the economy, thereby freeing up resources for its growth. Of course, the transition from traditional economic models to the model of 'green growth' can be realized only if significant efforts are made to expand international cooperation in this area. At the same time, there is a need for consistent implementation of events at various levels on the part of all states, as well as maintaining the chosen political course for many years.

Mitigating Faults and Revenue Losses Using Fault Detectors at Trans Amadi Industrial Layout, Port Harcourt Rivers State

Abstract: Transmission line fault detection is an important aspect of monitoring the health of a power plant since it indicates when suspected faults could lead to catastrophic equipment failure. This research looks at how to detect generator and transmission line failures early and investigates fault detection methods using Artificial Neural Network approaches. Monitoring generator voltages and currents, as well as transmission line performance metrics, is a key monitoring criterion in big power systems. Failures result in system downtime, equipment damage, and a high danger to the power system's integrity, as well as a negative impact on the network's operability and dependability. As a result, from a simulation standpoint, this study looks at fault detection on the Trans Amadi Industrial Layout lines. In the proposed approach, one end's three phase currents and voltages are used as inputs. For the examination of each of the three stages involved in the process, a feed forward neural network with a back propagation algorithm has been used for defect detection and classification. To validate the neural network selection, a detailed analysis with varied numbers of hidden layers was carried out. Between transmission lines and power customers, electrical breakdowns have always been a source of contention. This dissertation discusses the use of Artificial Neural Networks to detect defects in transmission lines. The ANN is used to model and anticipate the occurrence of transmission line faults, as well as classify them based on their transient characteristics. The results revealed that, with proper issue setup and training, the ANN can properly discover and classify defects. The method's adaptability is tested by simulating various defects with various parameters. The proposed method can be applied to the power system's transmission and distribution networks. The MATLAB environment is used for numerous simulations and signal analysis. The study's main contribution is the use of artificial neural networks to detect transmission line faults. Keywords: Faults and Revenue Losses

Analysis of Power Supply Reliability of Power System Distribution Network

Analysis of Power Supply Reliability of Power System Distribution Network

Optimal power flow-path determination for voltage control in electricity distribution using the modified dijkstra’s algorithm

This paper examines the electric power distribution network system of the Port Harcourt Electricity Distribution Company (PHEDC); its shortcomings, costs and voltage loss in distribution with a view to finding optimal solution through determination of optimal power flow path. The Modified Dijsktra’s Algorithm was applied to generate optimal flow path model of the distribution network with seven (7) nodes from Afam Thermal Power Station (source) to the Calabar Distribution Centre (destination) via the interconnected substations. The structural design of the PHEDC distribution network and a review of relevant literatures on shortest path problems were adopted. The modified Dijkstra’s algorithm was simulated using JavaScript and is able to run on any web browser (Google Chrome, Mozilla Firefox, etc). It was applied to a practical 330kV network using the relevant data obtained from the company and the result shows the negative effect of distance on voltage quality. It was observed that the Modified Dijkstra’s Algorithm is suitable for determining optimal power flow path with up to 98 percent level of accuracy because of its suitability for determining the shortest route in both transportation and power energy distribution as well as its overall performance with minimal memory space and fast response time.

Calculation and Analysis of Heat Field of Large Capacity Epoxy Pouring Dry-type Transformer Based on Fluid-solid Coupling

Dry-type transformer had the advantages of simple structure, easy maintenance, small environmental pollution, safe and reliable operation. It was widely used in power system distribution network. With the increase of urban power load year by year, large capacity dry type transformer had been highly concerned by the power industry. In this paper, the fluid-solid coupling calculation equation of a large capacity epoxy casting dry-type transformer was expounded, and a two-dimensional axisymmetric model of the transformer was established. The finite element method was used to calculate the temperature distribution and hot spot temperature with different wind speed and load rate. The results showed that when the wind speed at the transformer inlet was 0.7m/s and the load exceeded 120%, the temperature rise of the low-voltage winding reached 136.41°C, which had exceeded the temperature limit of the material. Therefore, the ventilation volume needed to be increased and the running time needed to be reduced. The research results could provide technical support for the design and safe operation of large capacity epoxy casting dry type transformer.

Fault classification in power system distribution network integrated with distributed generators using CNN

Fault detection is the critical stage of the relaying system and their successful completion in minimum time is expected for fault clearance. With the increasing usage of distributed generators (DGs) in a distribution network, the conventional relaying methods are becoming inappropriate due to changing fault current levels. This paper presents a deep learning algorithm i.e. Convolutional Neural Network (CNN) customized for fault classification in the distributed networks integrated with DGs. This is first time that CNN has been used for fault detection using raw and sampled-data of three-phase voltage and current signals of various fault classes and no-fault class. The 10-fold cross-validation is used to demonstrate the performance of the proposed model in terms of different metrics such as accuracy, sensitivity, specificity, precision, and F1 score. The proposed model has attended an average 10-fold cross-validation accuracy of 99.52% for all the tested fault cases. This featureless proposed method has been compared with conventional approaches from literature and has shown better performance in terms of accuracy and computation burden. Further, a similar fault study is conducted on a mixed transmission line and distribution network with PV as DG using the proposed method and found performance accuracy of 99.92% and 99.97%, respectively.

Dielectric response measurement on service-aged XLPE cables: From very low frequency to power frequency

Polymeric-insulated cables, especially cross-linked polyethylene (XLPE) cables, are widely used in power system transmission and distribution networks. A cable failure can result in lengthy outages and substantial economic loss to the electrical utilities. One of the primary reasons for cable fault is insulation degradation over the service time. Various onsite electrical, thermal or mechanical stresses can accelerate the aging process of power apparatus and curtail the lifetime [1]–[3]. Additionally, different chemical processes can alter the morphological structure or chemical composition of the insulating material and degrade the dielectric properties [3], [4]. Therefore, from economic and technical points of view, regular condition monitoring and diagnostic testing have become critically important for checking the health of the insulation system before the occurrence of any complete failure. Moreover, diagnostic testing performed at appropriate intervals coupled with proper maintenance helps extend the lifespan of high voltage power equipment and components [5]–[8].

An approach of multistage distribution system network expansion planning considering investment, operational cost and reliability of system

Objectives: To solve the Multistage Distribution System Planning (MSDSP) problem by a solution strategy based on investment cost, operational cost and reliability of electric power distribution system network. Method: A distinctive multi- objective function model has been developed to compute the expansion period wise cost of investment, operational cost (cost of energy losses in the distribution network) and system reliability is evaluated in terms of Non-Supplied Energy Cost (NSEC) under contingency conditions. Findings:The computed result presents the cost of investment of new equipment, operational cost and NSEC for each period of network expansion along with overall cost of investment in all periods of network expansion. Results obtained also demonstrate the corresponding bus nodes voltages in per unit (pu) for each period of network expansion to satisfy the technical constraints of the voltage profiles. Novelty: A distinctive multi-objective function model is developed to solve MSDSP problem with planning & operational constraints and results obtained encourages the prospects related to the technical, economical and system reliability issues of electric power distribution system network. Keywords: Multistage distribution system planning (MSDSP); investment cost;operational cost; distribution system reliability; non-supplied energy cost(NSEC); load flow studies