Outages In Distribution Networks Research Articles

Reconfiguration of the real distribution network and providing a new resilience index, considering the uncertainty of renewable energy sources and digital protection devices in order to improve the resilience

Electric energy, as the most widely used form of energy, has always captured human attention to itself; however, with the climate change and the increase of natural phenomena, it is now necessary to provide solutions to improve the resilience of the network. Resilience is defined as the capability of the system to maintain an acceptable level of performance against a severe disturbance and return to its original condition in an appropriate period of time, which demonstrates the degree of endurance, vulnerability, and reversibility of an infrastructure system. The occurrence of adverse weather conditions and natural disasters causes extensive losses and power outages in distribution networks; and the number and severity of such incidents have seen an increase in recent years. Therefore, it is of great significance to evaluate the resilience of the network and its reversibility capability in dealing with adverse weather conditions. In this paper, after defining the concept of resilience in distribution networks, its characteristics are determined; and then, modeling common natural events such as floods and storms, and introducing a new index to evaluate resilience in distribution networks with digital directional overcurrent relays (DOCR) and Digital dual-setting directional overcurrent relays (DS-DOCR) in the presence of renewable energy sources (RES), the proposed scheme is presented.

State of Risk Prediction for Management and Mitigation of Vegetation and Weather Caused Outages in Distribution Networks

State of Risk Prediction for Management and Mitigation of Vegetation and Weather Caused Outages in Distribution Networks

Deep learning-based neural networks for day-ahead power load probability density forecasting.

Energy efficiency is crucial to greenhouse gas (GHG) emission pathways reported by the Intergovernmental Panel on Climate Change. Electrical overload frequently occurs and causes unwanted outages in distribution networks, which reduces energy utilization efficiency and raises environmental risks endangering public safety. Electrical load, however, has a dynamically fluctuating behavior with notoriously nonlinear hourly, daily, and seasonal patterns. Accurate and reliable load forecasting plays an important role in scheduling power generation processes and preventing electrical systems from overloading; nevertheless, such forecasting is fundamentally challenging, especially under highly variable power load and climate conditions. This study proposed a deep learning-based monotone composite quantile regression neural network (D-MCQRNN) model to extract the multiple non-crossing and nonlinear quantile functions while conquering the drawbacks of error propagation and accumulation encountered in multi-step-ahead probability density forecasting. The constructed models were assessed by an hourly power load series collected at the electric grid center of Henan Province in China in two recent years, along with the corresponding meteorological data collected at 16 monitoring stations. The results demonstrated that the proposed D-MCQRNN model could significantly alleviate the time-lag and biased-prediction phenomena and noticeably improve the accuracy and reliability of multi-step-ahead probability density forecasts on power load. Consequently, the proposed model can significantly reduce the risk and impact of overload faults and effectively promote energy utilization efficiency, thereby mitigating GHG emissions and moving toward cleaner energy production.

A hybrid group-making decision framework for regional distribution network outage loss assessment based on fuzzy best-worst and MARCOS methods

Scientifically assessing the outage loss of regional distribution networks is conducive to the supplying-power enterprises and government to formulate development plans. The purpose of this study is to develop a systematic group decision-making framework to effectively solve the problem of outage loss assessment. Firstly, a multi-dimensional index system containing qualitative and quantitative attributes is established. Then, a relative entropy-based (RE-based) approach that incorporates fuzzy proximity and fuzzy similarity entropy weights is employed to determine experts’ dynamic weights. Subsequently, the fuzzy best-worst method (BWM) is presented to identify optimal criteria weights, thereby retaining the originality of weight information. Finally, the alternatives are rank-ordered based on the fuzzy MARCOS (Measurement of Alternatives and Ranking according to COmpromise Solution) method by determining utility functions in relation to ideal and anti-ideal solutions. A case study is conducted based on the developed framework. The sufficient sensitivity analysis illustrates that the method adopted in this paper provides good robustness to fluctuations in criteria weights, and comparative analysis verifies the applicability and effectiveness of the framework. This study offers the scientific basis and decision support for the deployment of reducing regional distribution network outage loss and enriches the research methods and practical applications of the decision-making field. • A hybrid framework is given for regional distribution network outage loss assessment. • A comprehensive index system with qualitative and quantitative attributes is built. • The dynamic weights of experts are obtained by combining fuzzy PEW and fuzzy SEW. • Fuzzy best-worst method is adopted to determine the weights of evaluation attributes. • The fuzzy MARCOS enables a more flexible and effective decision-making.

Comprehensive assessment and early warning of the risk of repeated power outages in distribution networks based on multi-source information fusion

In order to reduce the occurrence of power outages with repeated occurrences in the distribution network, this paper first defines the quantitative index of the concept of repeated occurrences. The FP-Growth data mining algorithm based on the optimal frequent itemset is proposed to obtain the correlation between the power outage factors. Combined with the fuzzy analytic hierarchy process, the main reasons for repeated power outages are obtained. The analytic hierarchy process combining subjective and objective weights is used to comprehensively evaluate the risk of repeated power outages in the distribution network. This paper realizes the early warning of power outage risk based on the comprehensive probability value of power outage. Finally, an actual distribution network in Chongqing is taken as an example to verify the practicability and effectiveness of the method.

Research on load loss prediction of distribution network outage based on hybrid neural network

In order to fully mine the relationship between temporal characteristics in load data and improve the accuracy of load forecasting, a load forecasting method based on convolutional neural networks (CNN) and gated recurrent unit (Gru) hybrid neural network is proposed. Taking date factors, climate factors and similar daily load factors as input, the sample data sets in the region are grouped by K-means clustering method; Then cnn network is used to extract the relationship between features and load in high-dimensional space, construct the high-dimensional feature vector of time series, and input the results into Gru network; Finally, the parameters of Gru network model in each group are trained and the load forecasting value is output. The results show that the proposed load forecasting method has significant advantages in forecasting accuracy and efficiency compared with long short term memory (LSTM) network model, Gru network model, cnn-lstm network model, support vector machine regression model and decision tree model.

A Multi-Step Reconfiguration Model for Active Distribution Network Restoration Integrating DG Start-Up Sequences

The ever-increasing penetration of Distributed Generators (DGs) in distribution networks suggests to enable their potentials in better fulfilling the restoration objective. The objective of the restoration problem is to resupply the maximum energy of loads considering their priorities using minimum switching operations. Basically, it is desired to provide a unique configuration that is valid regarding the load and generation profiles along the entire restorative period. However, this unique configuration may not satisfy at the same time: I) the DG start-up requirements at the beginning of the restoration plan and II) the topological conditions that would allow the DG to provide later on the most efficient support for the supply of loads. Therefore, it is proposed in this paper to allow a limited number of reconfiguration steps according to the DG start-up requirements. In addition, this paper presents a novel formulation for the reconfiguration problem that accounts for partial restoration scenarios where the whole unsupplied area cannot be restored. The decision variables of the proposed multi-step restoration problem are: I) the line switching actions at each step of the reconfiguration process, II) the load switching actions during the whole restorative period and, III) the active/reactive power dispatch of DGs during the whole restorative period. A relaxed AC power flow formulation is integrated to the optimization problem in order to ensure the feasibility of the solution concerning the operational safety constraints. The overall model is formulated in terms of a mixed-integer second-order cone programming. Two simulation scenarios are studied in order to illustrate different features of the proposed strategy and to demonstrate its effectiveness particularly in the case of large-scale outages in distribution networks.

The Feasibility Research on Distribution Network Closed Loop Based on the Load Transfer Model

With the continuous development of urban distribution network, most of the distribution network has formed a dual power supply mode. The traditional way of distribution network load operating mainly adopts the power method, while satisfied the requirements of the safety of power grid, but will cause external short time power outages, poor user experience, realizing the distribution network outage rearrangement of load is a necessary means to improve power supply reliability. This paper presents mathematical model of load transfer in distribution network. The differences of voltage of amplitude and phase angle on both sides are calculated by the power flow. According to the differential pressure to determine whether the loop can be closed at the loop operation, thereby improving the success rate of operation.

An Outage Risk Oriented Dynamic Distribution Network Reconfiguration Methodology Considering the Effects of Weather Conditions on Power Line Failure Rate

A dynamic network reconfiguration methodology considering time-varying weather conditions is proposed. The aim of the proposed methodology is to minimize outage risk. To fully reflect actual weather severity, a classification method for weather states based on weather variables analysis is presented. Considering time-varying nature of weather, an approach to determine the optimal time for reconfiguration by comparing reliability change between adjacent intervals is provided. Then, applying the Quantum Genetic Algorithm to solve reconfiguration optimization problem is introduced. Simulation results on the IEEE 33-node test systems show that the proposed dynamic reconfiguration method can reduce distribution network's outage risk. In addition, weather conditions have impacts on outage risk of distribution network and should be considered in reconfirmation problems.

White stork risk mitigation in high voltage electric distribution networks

White stork (Ciconia ciconia L.) population increased dramatically in Portugal over the last decade, leading to an increase in nesting and roosting in the support towers for the electric distribution lines. As a consequence, there are frequent bird fatalities from electrocution and outages in distribution networks are recurrent. Consequently, this causes a poor power quality service and equipment damages, with resulting economic losses for industrial customers and electric distribution companies. To mitigate the risk of stork fatalities and to improve the quality of service, the Portuguese Distribution System Operator (DSO), Energias de Portugal – Distribuição (EDP-D) and Energy Service Regulatory Authority (ERSE) in collaboration with the Institute for Nature Conservation and Forests (ICNF) implemented programs to control white storks activities on power distribution equipment.To control nesting activities several models of electrified structures, based on a micro electroshock discharge system, were developed and applied on the top of the line support towers. The installed structures proved to be successful, since no nesting activities were detected.When landing on the structure, the white stork receives a small shock, which is associated with a negative experience, discouraging further attempts to roost on the support structure. Compared to other technologies tested in the past, taking into account maintenance requirements, equipment costs and power supply requirements, the micro electroshock system proved to be the most reliable and advantageous solution.

Estimation of the overload‐related outages in distribution networks considering the random nature of the electrical loads

The overload phenomenon in distribution networks is a prevalent event, which usually causes the unwanted outages happen in distribution networks. The malfunction of the protection system as a result of high load currents during the overload situations yields network outages which are often unpredictable. The reliability indices in distribution systems are currently calculated neglecting the effect of overload condition. Having a correct estimation of the number of the overload-related outages in distribution networks enables the network planners and operators to better choice a proper solutions to avoid or decrease the side-effects of this event. In this study, in order to estimate the outage rates in distribution networks because of the overload phenomenon, a stochastic model for the electrical loads is presented. The normal cumulative distribution function is used to model the magnitude of the load demand at any time of the day. Monte Carlo approach is applied for random selection of the load magnitude. Load model results are compared with real measured data and the validity of the results is investigated. Electrical loads of the IEEE 34-bus system are modified by incorporating the real load variation curves to provide more realistic results. The results are presented and analysed in detail.

Evaluation of Distribution Network Customer Outage Costs

Evaluation of Distribution Network Customer Outage CostsCustomer outage cost criteria are considered, collected and analyzed outage costs in Latvia distribution network, as well as distribution network outage elimination structure, the most common outage causes, are proposed outage costs estimation model. Finally the discussion of results of expected customer outage costs and interrupted energy assessment rate calculation results in Latvia distribution network in 2007 are presented, based on customers' mean value of incomes, outcomes and profitability.