Distribution Network Maintenance Research Articles

Switch placement algorithm to reduce the impact of distribution network maintenance in continuity indexes

switch placement algorithm to reduce the impact of distribution network maintenance in continuity indexes

Multi-objective Collaborative Optimization Method for Intelligent Distribution Network Operation and Maintenance

Most of the conventional multi-objective coordination methods for active distribution network operation are designed based on the principle of weighted random algorithm, which has limited application scope and cannot simplify the algorithm and reduce the complexity of the solution process, resulting in poor stability of active distribution network lines and potential faults. Based on this, this algorithm is introduced to carry out multi-objective collaborative optimization for intelligent distribution network operation. Firstly, the node types for the intelligent distribution network are defined and the power flow of the distribution network is analyzed. Secondly, a multi-objective configuration model for the lower level of the operation is constructed to maximize the distribution network investment and operation benefit index. A multi-objective collaborative optimization algorithm based on an enhanced particle swarm optimization algorithm is designed to reduce the operation cost of the distribution network and enhance the security and stability of the distribution network system during operation. The simulation test results show that after the application of the new method, the intelligent distribution network voltage stability index Vm is always less than 1, and the line stability has been significantly improved.

Improving Water Distribution Network Models with Data Assimilation: Unleashing the Power of Real-Time Improvements

Ensuring a reliable water supply in the face of changing conditions and growing demand is a critical global challenge. Water distribution networks (WDNs) are essential infrastructure, but traditional modeling methods based on historical data often struggle to adapt in real-time and integrate new information. In order to lower model errors in WDN models, this study explores the use of a Data Assimilation (DA) method that makes use of the Ensemble Kalman Filter. The study explores the effectiveness of the DA method, and a novel Greedy Algorithm (GA) for optimizing the location of sensors. The study shows that the DA method improves the WDN model and the GA is able to successfully determine optimal sensor locations. It was observed that increasing the number of sensors in the WDN increased the effectiveness of the DA method. This study highlights the potential of data assimilation to improve WDN modeling. Water utilities can gain from more precise predictions, greater system performance, and efficient decision-making for the management and maintenance of water distribution networks by allowing models to adapt and improve dynamically

Assessment of risks to the quality of water supplied in Bushenyi-Uganda using the water safety plan approach

Abstract This study assessed the effects of environmental and operational hazardous events on the Bushenyi water supply system over the period 2017–2020. Monthly secondary water quality data for the period July 2013–December 2017 were analyzed together with data from field samples collected monthly from January 2018 to November 2020. The parameters analyzed were pH, turbidity, total iron, free chlorine and faecal coliforms. Hazardous events and risks affecting the water supply at the source, treatment and distribution system were identified and assessed during the field visits. Control measures were determined during water safety plan development effective July 2017 and implemented effective August 2018. Quality of water in the distribution system met the national standards for turbidity (93%), total iron (99%), residual free chlorine (90%) and faecal coliforms (96%). pH in the storage and distribution system was below the national standard (annual mean range, 5.5–6.7). Water quality was negatively influenced by extreme seasonal weather variations at the source, source protection gaps, treatment deficiencies related to clarifier, filter and chemical dose management as well as distribution management and maintenance gaps. Improved source protection, treatment and distribution network management and maintenance are recommended for sustainable system and water quality standards.

Open capacity measurement and evaluation of n-1 security criteria considering distribution network dispatching security

In order to ensure the safety of distribution network dispatching, the open capacity evaluation, one of the distribution network operation and maintenance tools, is analyzed. Aiming at the shortcomings of traditional reactive power capacity evaluation that is too simple and cannot effectively handle multiple interconnected feeders, a new method is proposed to consider N-1 to establish security and network reconfiguration. Considering the safety factor and the minimum network loss, the open capacity is evaluated, and the interconnection switch is regenerated, which provides the best scheme for the maintenance of the distribution network. This model not only considers the power flow constraints during normal operation but also considers the switching operation constraints under fault conditions. It has the advantages of high solution quality and feasible calculation results. In order to solve the model, a two-level hybrid algorithm based on generalized Benders decomposition, a dichotomous approximation is proposed. The algorithm adopts the idea of upper approximation, lower test, and two-layer interaction. It continuously reduces the solution space through alternate iterations until the optimal solution is obtained. The test results of 22-node and 70-node systems verify the correctness and effectiveness of the model and algorithm in this paper.

Fault type identification of arc grounding based on time-frequency domain characteristics of zero sequence current

Different types of faults pose different degrees of threat to the distribution network. Accurate identification of fault types is essential for distribution network maintenance and hazard prevention. The simulation of a typical single-phase arc grounding fault in a distribution network is carried out based on a 10 kV test platform, and the zero-sequence current of cable fault, tree touch, line break, and insulator flashover is obtained. The BP neural network is established and trained to recognize the feature data, which is extracted by Fourier transform and wavelet transform. The identification results prove the effectiveness of the proposed method for single-phase arc grounding fault type identification in distribution networks.

Pengukuran Tahanan Isolasi Terminal Incoming Gardu Kubikel 20KV di PT Haleyora Power Region 3 Jawa Timur Area Mojokerto

PT Haleyora Power is a subsidiary of PT PLN which is engaged in the operation and maintenance of electricity transmission and distribution networks. One of them is routine maintenance of the 20kv cubicle substation with the aim of maintaining the reliability of distribution in the electricity system. For Region 3 East Java, the Mojokerto area itself has 3 working areas, namely, Mojokerto, Jombang, and the Nganjuk area. In the process the cubicle is divided into 3 points, namely, incoming as input from the 20kv transformer output owned by PLN, metering is the part that functions to monitor (current, voltage, and electricity demand), the last is outgoing which is the 20kv output which will enter the transformer input customer/industry property. In this study, what was measured was insulation resistance using an insulation tester / megger and focused on the incoming terminal, the terminal points measured were between terminals r-s, r-t, s-t, r-n, s-n, and t-n. The minimum standard for measuring insulation resistance in a 20kv cubicle is 20MOhm. With an insulation resistance of more than 20MOhm, the insulation resistance obtained is good enough.

A Novel High-Voltage-Cable Stripping Robot

One of the primary duties in the regular maintenance of electrical distribution networks is the cable stripping operation. In this paper, a unique robot is proposed to overcome drawbacks of the conventional manual operation of cable stripping, such as poor efficiency, low safety, and high labor intensity. This innovative cable-stripping robot is made up of a rotating mechanism, a cable gripping component, and a cutter feeding mechanism that can be adjusted depending on the working environment and workload. The robot’s motors, sensors, main control chip, and wireless communication modules are all carefully selected. A carefully designed cascade controller is created for the robot in an effort to lessen damage to the aluminum core. While the outside location loop uses the PID algorithm, the inner speed control loop uses fuzzy PID. The robot can successfully accomplish cable stripping work and demonstrates its potential to reduce labor intensity. Cable stripping experiments are conducted to validate the effect of the robot and its controller.

Rewards, risks and responsible deployment of artificial intelligence in water systems

Artificial intelligence (AI) is increasingly proposed to address deficiencies across water systems, which currently leave about 25% of the global population without clean water, about 50% without sanitation services and about 30% without hygiene facilities. AI is poised to enhance supply insights, catchment management and emergency response, improve treatment plant and distribution network design, operation and maintenance, and advance service availability, demand management and water justice. However, proliferation of this nascent technology could trigger serious and unexpected problems, including system-wide compromise owing to design errors, malfunction and cyberattacks as well as exposures to cascading socio-ecological, water–energy–food nexus and coupled critical infrastructure failures. In response, we make three recommendations for safe and responsible deployment of AI across potable water supply and sewage disposal systems: address gaps in foundational infrastructure and digital literacy; establish institutional, software and hardware mechanisms for trustworthy AI; and prioritize applications based on our proposed systematic benefit and risk assessment framework. In this Perspective, the authors survey potential water-system-wide benefits of AI applications from catchments to end-users and then highlight potential systemic barriers, direct risks and exposures to cascading failures, which may prove catastrophic for communities. In response, they make three recommendations for safe and responsible deployment of AI across potable water supply and sewage disposal systems.

Structure Design and Performance Analysis of Insulator Replacement Device Based on Live Working in A Distribution Network

In order to solve the problems of high risk and low automation of manual insulator replacement, a live working insulator replacement device is developed. Firstly, according to the shape parameters of insulators, the mechanical structure is composed of a positioning mechanism, a limiting mechanism, a clamping mechanism, and a driving mechanism. Secondly, the control system of the device is designed, and the mechanism is controlled by wireless remote control. Thirdly, the static simulation analysis of the clamping mechanism is carried out by using the finite element analysis software ANSYS Workbench. The simulation results show that the maximum elastic deformation is 0.14366mm, the stress distribution is relatively uniform, and the strain is small, meeting the structural design requirements. Finally, the motion control function test and insulation performance test of the device are carried out. The results show that the designed device has good insulation performance. It meets the needs of daily maintenance and emergency repair of distribution networks below 10kV and has high promotion and application value.

Analysis of Maintenance Optimization on Medium Voltage Overhead Lines (SUTM) in Reducing Energy Not Supplied (ENS) at PT. PLN (Persero) ULP Tarakan

The background of this research is an analysis of equipment disturbances that occur in distributing electrical energy. The disturbance is a blackout. Power outages cause a decrease in the continuity of service to customers and result in a lot of loss of electrical energy that is not distributed to customers. The research objectives are to find out the optimization technique of distribution network maintenance, how to reduce the value of energy not supplied (ENS), find out the losses due to ENS, and find out the total electrical energy distributed after preventive maintenance is carried out. This research uses a preventive maintenance method, which is maintenance that can prevent unexpected damage to distribution system networks and equipment. This research optimized preventive maintenance actions by reducing disturbances after preventive maintenance from 7 disturbances in February to 2 disturbances in March. Before preventive maintenance, the loss due to ENS amounted to Rp. 36,192,017.626, and the total electrical energy not distributed was 25,025.76 kWh. After preventive maintenance, there was a significant decrease in the total loss due to ENS of Rp. 953,516,447, and the amount of electrical energy not distributed was 659.28 kWh. This research can increase maintenance efficiency due to electrical energy losses by 97.36% at the time interval when this research data is managed when data collection is carried out in the field.

Research on Cloud-Edge-Terminal function configuration based on differentiated low-voltage intelligent distribution network

At the present, more than 30 low-voltage intelligent distribution rooms have been built in Yunnan Power Grid, covering incoming cabinets, distribution substations, low-voltage switches and environmental monitoring, etc. The intelligent distribution rooms use the Cloud-Edge-Terminal architecture model for intelligent management, monitoring and control of low-voltage distribution rooms but there are generally problems such as low level of heterogeneous data application, insufficient correlation analysis capability, fault location. However, there are problems such as low level of heterogeneous data application, insufficient correlation analysis capability, inaccurate fault location, excessive network load, insufficient sensing capability of sensing terminals and unreasonable configuration. Based on the application model of low-voltage intelligent distribution network of Yunnan power grid, Our paper intends to summarize the experience and methods to support operation and maintenance, fault repair and customer service and study the differentiated low-voltage intelligent distribution Cloud-Edge-Terminal solution to improve the efficiency of intelligent distribution network operation and maintenance.

An Indirect Procedure for Real-Time Monitoring the Neutral Conductor Deterioration in Three-Phase Distribution Networks

An indirect procedure for real-time monitoring the neutral conductor condition in three-phase distribution networks, based on watching over the growth of a novel parameter (∆τ), has been described in this paper. The parameter ∆τ has been defined as the relationship between the neutral-displacement power and Buchholz’s apparent power measured at the fundamental frequency in the loads of the distribution networks for any condition of the neutral conductor and in its nominal conditions. The effectiveness of this procedure has been compared with other traditional indirect procedures, such as the surveillance of the RMS values of the line-to-neutral load voltages or their zero-sequence component. The practical application on a real distribution network reveals that the growth of the parameter ∆τ in the early stages of the breaking process of the neutral conductor follows a straight line whose equation is known for each length and section of that conductor, regardless of the loads and the voltage regulation of the transformer of the distribution network. This characteristic of the ∆τ parameter shows that the proposed procedure is suitable for monitoring neutral conductor deterioration and can be used for preventive maintenance of distribution networks.

A Reinforcement Learning-based Approach for Active Optimization of High Proportional Distributed Power Supply Access to Rural Distribution Networks

In the process of actively implementing the rural revitalization strategy and the “double carbon” goal, distributed power sources are connected to the rural distribution network on a large scale, resulting in a gradual transition to a new type of active distribution network, which has a greater impact on the distribution network in terms of voltage, network loss, and protection, thus posing greater demands on the distribution network in terms of planning and control, power quality management, and operation. This significantly impacts the voltage, network loss, and protection of the distribution network, thus putting higher requirements on the planning and control, power quality management, operation, and maintenance of distribution networks. Since the traditional OPF algorithm has great defects in solving large-scale decentralized, time-series, and stochastic nonlinear problems, in order to realize real-time control of distribution networks under more complex network conditions, this paper proposes an optimization algorithm based on reinforcement learning and applies it to real-time control of active distribution networks, taking the optimal network loss as the objective function and considering network voltage, line load factor, and distributed power supply. As a de-modeling artificial intelligence method, reinforcement learning has great advantages in solving large problems with complex mathematical models. The method’s control effect and convergence time can be improved by introducing imitation learning and migration learning, expanding the knowledge matrix, and introducing risk assessment mechanisms. Based on MATLAB R2017a simulation platform for the IEEE 33-node improvement model, the simulation results show that the reinforcement learning method has a better control effect in solving the real-time control problem of active distribution network, significantly alleviates the voltage crossing limit, effectively reduces the network loss, and has good real-time characteristics.

Decision-making for the hazard ranking of water distribution networks using the TOPSIS method

Abstract Owing to the consecutive occurrence of water quality incidents, such as the black and red water situation in Incheon, Mullae (Seoul), and Pohang, as well as the tap water larval events in Incheon and Jeju in 2020, South Korea's Ministry of Environment announced the new addition of Article 12-2 of the Enforcement Decree of the Water Supply and Waterworks Installation Act 2021. Additionally, the importance of intensive management for vulnerable areas where water quality incidents and complaints may occur repeatedly was specified. Therefore, this study suggests the hazard ranking of district-metered areas in the study area using the deterioration of the water distribution network, complaints (water quality, leakage), and modeling results (water pressure, residual chlorine concentration) to support the establishment of a reasonable water distribution network maintenance plan. The effective and sustainable maintenance of the water distribution network is possible if the hazard ranking is regularly determined using the suggested methodology proposed herein and the water distribution network intensive management area is managed.

Research on Intelligent Anti-misoperation of Operation Ticket in Urban Distribution Network

The traditional anti-mischeck mode of regulation and control operation relying on the experience of dispatchers can no longer meet the needs of daily work. To solve the problems of poor actual operation effect and low anti-misoperation efficiency of the traditional anti-misoperation method due to the large number of tasks of the current distribution network, this paper optimizes the traditional anti-misoperation method and takes the distribution network of M power supply company as an example to design a new intelligent anti-misapplication method of operation ticket. First of all, this paper uses intelligent distribution network maintenance technology to overhaul the distribution network operation ticket application server in an all-round way. Based on real-time analytical constraints, combined with the power supply demand of urban distribution networks, this paper designs an intelligent anti-misoperation constraint mechanism for operation tickets. The experimental analysis shows that after the application of the proposed anti-error method, the qualified rate of the operation order scheduling tasks is more than 95. 6% and the error rate is less than 1. 1%, which shows that the anti-error method proposed in this paper has significant advantages and high feasibility.

Intelligent Wireless Monitoring Technology for 10kV Overhead lines in Smart Grid Networks

Promoted by the rapid development of information technology, 10kV overhead line has been widely used in the majority of cities, and it is of great significance to monitor the distribution network effectively, in order to ensure the normal operation of the system. Most of traditional distribution network monitoring methods are based on manual work, which causes inconvenience to the distribution network fault location, repair, maintenance and real-time monitoring, and reduces the efficiency of the distribution network emergency repair and the reliability of power supply. Aiming at the automatic monitoring problem of 10kV overhead network, this paper adopts an intelligent wireless monitoring technology, where a monitoring node is employed to monitor the network transmission status through wireless links. We evaluate the system monitoring performance by using the metric of outage probability, depending on the wireless data rate over wireless channels. For the considered system, we derive analytical outage probability, in order to measure the system performance in the whole range of signal-to-noise ratio (SNR). The simulation results are finally presented to verify the analytical expressions on the system monitoring outage probability in this paper.

Research on sensing node location technology based on AOA and fingerprint recognition

In order to meet the needs of intelligent operation and maintenance of distribution networks, a large number of wireless sensors are deployed inside and outside the power grid for digital sensing and immediate control of the grid. However, once these wireless sensors fail, they will generate erroneous data, which affects the assessment of grid security and brings great hindrance to the automation of power system operation and maintenance. Therefore, we propose an AOA and fingerprint recognition-based sensing node location method to achieve accurate positioning of wireless sensing nodes to facilitate rapid troubleshooting. The method first enhances and denoises the signal characteristics of wireless sensing nodes to achieve pre-processing of wireless sensing fingerprint information and solve the problem of fingerprint feature recognition of data samples. After that we combine the node fingerprint information through deep learning models to achieve device type recognition of wireless sensing nodes. Finally, we design the AOA node localisation method, which uses the AOA coordinate system measurement model to perform distance measurement and coordinate conversion on the fingerprint features of wireless sensing nodes to achieve accurate and fast localisation of wireless sensing devices. The experiments prove that the method has a high accuracy rate in the identification and location of wireless sensing node devices, which can effectively improve the efficiency of grid automation operation and accurate troubleshooting.

Deep learning technology of Internet of Things Blockchain in distribution network faults

AbstractNowadays, the development of human society and daily life are inseparable from the power supply. Therefore, people also put forward higher requirements for the reliability of distribution network, but power companies can only passively deal with distribution network failures, which is a bottleneck for the improvement of distribution network reliability. The Internet of Things (IoT) is the best solution for online equipment status monitoring and basic data sharing for large, widely distributed, relatively fixed, and large numbers of equipment. The construction of the IoT for power distribution equipment faces many important problems, including the selection of networking, equipment selection, and interaction standards. When researching the implementation plan, research on the distribution of IoT market was carried out. Based on the grid, the idea of optimizing the investment selection plan of the power distribution using IoT was discussed, and a result verification model was established. After the completion of the theoretical part, a case study of medium-voltage distribution grid equipment management and medium-voltage distribution network equipment management based on the grid was carried out by taking a real enterprise application situation as an example. Realizing fault diagnosis of distribution network will not only provide decision support for operation and maintenance of distribution network for power companies, but also have great economic and social benefits. Aiming at the shortcomings of single data mining method in distribution network fault diagnosis, hybrid data mining method is proposed. First, rough set theory is used to reduce the original fault data and form a simplified rule set. Because of the non-linearity of distribution network fault and the strong learning ability, adaptability, and robustness of Bayesian network, Bayesian network can be used to classify distribution network faults. Therefore, a simplified fault diagnosis system is established in this paper, and its correctness is confirmed. Then, the learning and training are carried out by using Bayesian network to call the simplest rule set, which has the characteristics of short learning and training time and high diagnostic accuracy.

Designing of multi-objective optimal virtual power plant model for reliability enhancement in radial network: a case study of Indian power sector

One of the major driving factors in the shifting of the present grid paradigm to an active grid network is the reliability and resiliency of the utility network. With hefty investment in the distribution network protection and maintenance, the reliability of the feeders is considerably enhanced; however, large numbers of outages are still occurring every year which caused major production loss to the manufacturing sector. In this paper, the role of the solar grid-based Virtual Power Plant (VPP) is evaluated in the state power utility for the reliability enhancement and cost minimization using a multi-objective model based on MILP optimization. A 90 bus industrial feeder having automatic reclosers, DER, and DSM is selected on which the MCS method is utilized for computing reliability indices using the utility reliability parameters. The value of reliability indices such as EENS is declined by 68% by utilizing the VPP scenario. These values of this reliability index are fed into the multi-objective model for cost minimization. After running the optimization, the results reveal that the operational and the annual energy cost are reduced by 61% and 55% respectively which advocates the VPP implementation in the utility network. Both modes of the Virtual Power Plant such as grid-connected and autonomous mode have been discussed in detail. Lastly, the results of the developed model with MILP are compared with the proprietary derivative algorithm, and it is found that the proposed MILP is more cost-effective. The overall results advocate the VPP implementation in the utility grid as the economical advantage is provided to both utility and the consumers in terms of reduction in EENS and energy charges respectively.