Integration of neural network and distance relay to improve the fault localization on transmission lines

Power transmission lines are integral and very important components of power systems. Because of the length of these lines and the complexity of the power grids, the lines may encounter various incidents such as lightning strike, shortage, and breakage. When an incident or a fault occurs, a fast process of identification, localization, and isolation of the fault is desired. An accurate fault localization would have a great impact in reducing the restoration time of the system. One of the most popular solutions for fault detection and localization is the distance relays using the impedance-based algorithms. However, these relays are still not perfect with nonzero errors of the fault locations. This paper will present a new approach using the neural networks in addition to a distance relays to correct the fault location estimation of the relay. The solution will be based only on the voltage and current signals measured at the beginning of the lines. The training samples' signals of the transient states on the lines are generated using ATP/EMTP, and then regenerated into the relay tester Omicron CMC-356 to test with the real Siemens 7SA522 relay to improve its fault location results. The numerical results will show that the solution had helped to reduce the average fault location error from 0.92% to 0.42% for 4 types of shortage faults on the lines. [ FROM AUTHOR] Copyright of Turkish Journal of Electrical Engineering & Computer Sciences is the property of Scientific and Technical Research Council of Turkey and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Assessment of effective reactive power reserve in power system networks under uncertainty applying coronavirus herd immunity optimizer (CHIO) for operation simulation

The Reactive Power Reserve (RPR) is a very important indicator for voltage stability and is sensitive to the operating conditions of power systems. Thorough understanding of RPR, specifically Effective Reactive Reserve (ERR) under intermittent Wind Power (WP) and uncertain demand is essential and key focus of this research. Hence, a stochastic multivariate ERR assessment and optimization problem is introduced here. The proposed problem is solved in three stages: modeling of multivariate uncertainty, studying the stochastic behavior of ERR and optimizing ERR. The volatilities associated with WP generation and consumer demand are modeled explicitly, and their probability distribution function is discretized to accommodate structural uncertainty. A combined load modeling approach is introduced and extended further to accommodate multi-variability. The impact of these uncertainties on ERR is assessed thoroughly on modified IEEE 30 and modified Indian 62 bus system. A non-linear dynamic stochastic optimization problem is formulated to maximize the expected value of ERR and is solved using ‘Coronavirus Herd Immunity Optimizer (CHIO)'. The impact of the proposed strategy on stability indices like the L-index, Proximity Indicator (PI) are analyzed through various case studies. Further, the effectiveness of the proposed approach is also compared with the existing mean value approach. Additionally, the performance of CHIO is confirmed through exhaustive case studies and comparisons. © 2023 Elsevier B.V.

Data-Driven Resilient Load Forecasting Model for Smart Metered Distribution System

The change in the electricity demand pattern globally due to sudden extreme weather conditions or situations like COVID 19 pandemic has brought unanticipated challenges for the electric utilities and operators around the world. This work primarily deals with the issue of load forecasting during such type of high impact low frequency (HILF) events. In this paper, we propose a novel resilient short-term load forecasting model capable of producing good forecasting performance for normal as well as critical situations during the COVID 19 pandemic and will also be useful for load forecasting for other HILF situations like natural calamity effect on load demand of the power system. The proposed method uses a feed-forward neural network (FFNN) with an added training feature named resiliency factor to forecast load in both regular and special scenarios. The resiliency factor for any type of node in the distribution system is decided by the power utility using the historical data and declared in advance. The proposed model is tested using the smart metered data available from a real-life distribution grid of an academic cum residential campus. The model is giving satisfactory results for both normal as well as COVID scenario for the said network. © 2023 Taylor & Francis Group, LLC.

Analysis of Pre- and during COVID-19 Mixed Load Models on Unbalanced Radial Distribution System Using a New Metaphor-Less Rao Optimization

An unbalanced electrical distribution system (DS) with radial construction and passive nature suffers from significant power loss. The unstable load demand and poor voltage profile resulted from insufficient reactive power in the DS. This research implements a unique Rao algorithm without metaphors for the optimal allocation of multiple distributed generation (DG) and distribution static compensators (DSTATCOM). For the appropriate sizing and placement of the device, the active power loss, reactive power loss, minimum value of voltage, and voltage stability index are evaluated as a multiobjective optimization to assess the device's impact on the 25-bus unbalanced radial distribution system. Various load models, including residential, commercial, industrial, battery charging, and other dispersed loads, were integrated to develop a mixed load model for examining electrical distribution systems. The impact of unpredictable loading conditions resulting from the COVID-19 pandemic lockdown on DS is examined. The investigation studied the role of DG and DSTATCOM (DGDST) penetration in the electrical distribution system for variations in different load types and demand oscillations under the critical emergency conditions of COVID-19. The simulation results produced for the mixed load model during the COVID-19 scenario demonstrate the proposed method's efficacy with distinct cases of DG and DSTATCOM allocation by lowering power loss with an enhanced voltage profile to create a robust and flexible distribution network. Copyright © 2023 Jitendra Singh Bhadoriya et al.

Developing a solution algorithm for the autonomous electric vehicle routing problem considering the mixed fleet

In recent years, some phenomena such as the COVID-19 pandemic have caused the autonomous vehicle (AV) to attract much attention in theoretical and applied research. This paper addresses the optimization problem of a heterogeneous fleet that consists of autonomous electric vehicles (AEVs) and conventional vehicles (CVs) in a Business-to-Consumer (B2C) distribution system. The absence of the driver in AEVs results in the necessity of studying two factors in modeling the problem, namely time windows in the routing plan and different compartments in the loading space of AEVs. The arrival and departure times of the AEV at the customer’s location must be pre-planned, because, the AEV is not able to decide what to do if the customer is late at this point. Also, due to increasing the security of the loads inside the AEVs and the lack of control of the driver during the delivery of the goods, each customer should only have access to his/her orders. Therefore, the compartmentation of the AEV’s loading area has been proposed in its conceptual model. We developed a mathematical model based on these properties and proposed a hybrid algorithm, including variable neighborhood search (VNS) via neighborhood structure of large neighborhood search (LNS), namely the VLNS algorithm. The numerical results shed light on the proficiency of the algorithm in terms of solution time and solution quality. In addition, employing AEVs in the mixed fleet is considered to be desirable based on the operational cost of the fleet. Author

Information Approach for Change Point Analysis of EGGAPE Distribution and Application to COVID-19 Data

The exponentiated generalized Gull alpha power exponential distribution is an extension of the exponential distribution that can model data characterized by various shapes of the hazard function. However, change point problem has not been studied for this distribution. In this study, the change point detection of the parameters of the exponentiated generalized Gull alpha power exponential distribution is studied using the modified information criterion. In addition, the binary segmentation procedure is used to identify multiple change point locations. The assumption is that all the parameters of the EGGAPE distributions are considered changeable. Simulation study is conducted to illustrate the power of the modified information criterion in detecting change point in the parameters with different sample sizes. Three applications related to COVID-19 data are used to demonstrate the applicability of the MIC in detecting change point in real life scenario.

Statistical Inference to the Parameter of the Inverse Power Ishita Distribution under Progressive Type-II Censored Data with Application to COVID-19 Data

The goal of the article is the inference about the parameters of the inverse power ishita distribution (IPID) using progressively type-II censored (Prog–II–C) samples. For IPID parameters, maximum likelihood and Bayesian estimates were obtained. Two bootstrap “confidence intervals” (CIs) are also proposed in addition to “approximate confidence intervals” (ACIs). In addition, Bayesian estimates for “squared error loss” (SEL) and LINEX loss functions are provided. The Gibbs within Metropolis–Hasting samplers process is used to provide Bayes estimators of unknown parameters also “credible intervals” (CRIs) of them by using the “Markov Chain Monte Carlo” (MCMC) technique. Then, an application of the suggested approaches is considered a set of real-life data this data set COVID-19 data from France of 51 days recorded from 1 January to 20 February 2021 formed of mortality rate. To evaluate the quality of the proposed estimators, a simulation study is conducted.

Household Electricity Consumer Classification Using Novel Clustering Approach, Review, and Case Study

There is an increasing demand for electricity on a global level. Thus, the utility companies are looking for the effective implementation of demand response management (DRM). For this, utility companies should know the energy demand and optimal household consumer classification (OHCC) of the end users. In this regard, data mining (DM) techniques can give better insights and support. This work proposes a DM-technique-based novel methodology for OHCC in the Indian context. This work uses the household electricity consumption (HEC) of 225 houses from three districts of Maharashtra, India. The data sets used are namely questionnaire survey (QS), monthly energy consumption (MEC), and tariff orders. This work addresses the challenges for OHCC in energy meter data sets of the conventional grid and smart grid (SG). This work uses expert classification and clustering-based classification methods for OHCC. The expert classification method provides four new classes for OHCC. The clustering method is employed to develop eight different classification models. The two-stage clustering model, using K-means (KM) and the self-organizing map (SOM), is the best fit among the eight models. The result shows that the two-stage clustering of the SOM with the KM model provides 88% of overlap-free samples and 0.532 of the silhouette score (SS) mean compared to the expert classification method. This study can be beneficial to the electricity distribution companies for OHCC and can offer better services to consumers.

Exponentiated Gull Alpha Exponential Distribution with Application to COVID-19 Data

In this paper, the main aim is to define a statistical distribution that can be used to model COVID-19 data in Mexico and Canada. Using the method of exponentiation on the gull alpha exponential distribution introduces a new distribution with three parameters called the exponentiated gull alpha power exponential (EGAPE) distribution. The distribution has the benefit of being able to represent monotonic and nonmonotonic failure rates, both of which are often seen in dependability issues. It is possible to determine the quantile function as well as the skewness, kurtosis, and order statistics of the suggested distribution. The approach of maximum likelihood is used in order to calculate the parameters of the model, and the RMSE and average bias are utilised in order to evaluate how successful the strategy is. In conclusion, the flexibility of the new distribution is demonstrated by modeling COVID-19 data. From the practical application, we can conclude that the proposed model outperformed the competing models and therefore can be used as a better option for modeling COVID-19 and other related datasets.

Global Identification of Unelectrified Built-Up Areas by Remote Sensing

Access to electricity (the proportion of the population with access to electricity) is a key indica for of the United Nations’ Sustainable Development Goal 7 (SDG7), which aims to provide affordable, reliable, sustainable, and modern energy services for all. Accurate and timely global data on access to electricity in all countries is important for the achievement of SDG7. Current survey-based access to electricity datasets suffers from short time spans, slow updates, high acquisition costs, and a lack of location data. Accordingly, a new method for identifying the electrification status of built-up areas based on the remote sensing of nighttime light is proposed in this study. More specifically, the method overlays global built-up area data with night-time light remote sensing data to determine whether built-up areas are electrified based on a threshold night-time light value. By using our approach, electrified and unelectrified built-up areas were extracted at 500 m resolution on a global scale for the years 2014 and 2020. The acquired results show a significant reduction in an unelectrified built-up area between 2014 and 2020, from 51,301.14 km2 to 22,192.52 km2, or from 3.05% to 1.32% of the total built-up area. Compared to 2014, 117 countries or territories had improved access to electricity, and 18 increased their proportion of unelectrified built-up area by >0.1%. The identification accuracy was evaluated by using a random sample of 10,106 points. The accuracies in 2014 and 2020 were 97.29% and 98.9%, respectively, with an average of 98.1%. The outcomes of this method are in high agreement with the spatial distribution of access to electricity data reported by the World Bank. This study is the first to investigate the global electrification of built-up areas by using remote sensing. It makes an important supplement to global data on access to electricity, which can aid in the achievement of SDG7.

Agriculture, Development and Sustainability in the Covid-19 Era

Domestic and international mobility restrictions helped to reduce the numbers of confirmed Covid-19 cases until the end of 2021. Indonesia entered 2022 with caution, however, as Omicron cases began to rise. Recent success in managing the pandemic has coincided with what might be the start of an economic recovery, in no small part driven by high commodity prices—mainly for coal and palm oil—improving the fiscal and trade balances. The new tax harmonisation law is intended to lower the fiscal deficit to less than 3% of GDP by 2023, and a carbon tax will be implemented in April 2022—starting with a cap-and-tax scheme for coal power plants, before more sectors are included. Agriculture has played a key role in helping Indonesia to weather the pandemic, with the sector’s growth supporting employment and food consumption during the crisis. A resurgence in the palm oil price, together with rising agricultural wages and a narrowing of the labour productivity gap, has helped the agriculture sector lead the recovery, but concerns remain over the sector’s environmental footprint. Against recent food and environmental policy commitments, a renewed focus on increasing on-farm yields is a critical area for policy. We conclude with some reflections on the national palm oil replanting program and how better benefits might be delivered for smallholders and the environment.

Resilience Analysis of Australian Electricity and Gas Transmission Networks

Given they are two critical infrastructure areas, the security of electricity and gas networks is highly important due to potential multifaceted social and economic impacts. Unexpected errors or sabotage can lead to blackouts, causing a significant loss for the public, businesses, and governments. Climate change and an increasing number of consequent natural disasters (e.g., bushfires and floods) are other emerging network resilience challenges. In this paper, we used network science to examine the topological resilience of national energy networks with two case studies of Australian gas and electricity networks. To measure the fragility and resilience of these energy networks, we assessed various topological features and theories of percolation. We found that both networks follow the degree distribution of power-law and the characteristics of a scale-free network. Then, using these models, we conducted node and edge removal experiments. The analysis identified the most critical nodes that can trigger cascading failure within the network upon a fault. The analysis results can be used by the network operators to improve network resilience through various mitigation strategies implemented on the identified critical nodes.

Economic Effects of Micro- and Mini-Distributed Photovoltaic Generation for the Brazilian Distribution System

The micro- and mini-distributed generation (MMDG) has significantly increased after the normative resolution No. 482/2012 in Brazil;the installed capacity surpassed 7 GW in 2021. In the international context, a similar event was observed, whose process generated a cross-subsidy for other consumers, in addition to other problems that affect the economic balance of concessionaires. To mitigate this issue, the National Electric Energy Agency (ANEEL) is in the process of revising current rules. Thus, this study estimates the weight of this decision, through a methodology adapted from international assessment models, based on information from the Brazilian regulatory system. In order to achieve it, this paper presents metrics to define the potential market MMDG, based on the consumption patterns of consumers. Then, through time series analysis, the MMDG demand curve is estimated under two scenarios up to 2030. Finally, the economic impact on tariff adjustments and revisions, and their effect on the electric power concessionaires are evaluated. In the distribution companies of the Enel Group alone, economic losses are estimated at USD 1.2 billion by 2030;53% of this will be passed on to consumers’ tariffs. Thus, based on international experiences, it can be concluded that the best model is the adequate grid remuneration.

Energy Resilience Impact of Supply Chain Network Disruption to Military Microgrids

The ability to provide uninterrupted power to military installations is paramount in executing a country’s national defense strategy. Microgrid architectures increase installation energy resilience through redundant local generation sources and the capability for grid independence. However, deliberate attacks from near-peer competitors can disrupt the associated supply chain network, thereby affecting mission critical loads. Utilizing an integrated discrete-time Markov chain and dynamic Bayesian network approach, we investigate disruption propagation throughout a supply chain network and quantify its mission impact on an islanded microgrid. We propose a novel methodology and an associated metric we term “energy resilience impact” to identify and address supply chain disruption risks to energy security. The proposed methodology addresses a gap in the literature and practice where it is assumed supply chains will not be disrupted during incidents involving microgrids. A case study of a fictional military installation is presented to demonstrate how installation energy managers can adopt this methodology for the design and improvement of military microgrids. The fictional case study shows how supply chain disruptions can impact the ability of a microgrid to successfully supply electricity to critical loads throughout an islanding event.

Preface to the Special Issue on “Applications of Fuzzy Optimization and Fuzzy Decision Making”

The objective of this Special Issue is to expand the applicability of fuzzy optimization and decision making by applying state-of-the-art techniques from fuzzy technology, computational intelligence and soft-computing methodologies for solving real-life problems. [1] proposes a case-based reasoning method for the judgment of debtor’s hidden property analysis, which employs crisp and interval numbers as well as fuzzy linguistic variables, and develops a hybrid similarity measure to improve the efficiency of handling law enforcement cases. [4] the performance and efficiency of energy supply companies with respect to productivity are examined with reference to a case study of an electricity distribution company. The paper authored by Pérez [15] examines technological tables in electrical discharge machining to determine optimal operating conditions for process variables.

Does COVID‐19 outbreak cause spot electricity price discovery in India?

This paper aims to investigate whether COVID‐19 pandemic causes the spot electricity price discovery of the Indian electricity market. To do so, we use the average daily spot electricity price data for five regions of the Indian electricity market (North, East, West, South, and North‐East). The data is considered from March 15, 2020 to May 02, 2020. The results obtained from cross‐sectional augmented Im, Pesaran and Shin (CIPS) unit root test show the stationary of spot electricity price and COVID‐19 at the level. Additionally, we use the Dumitrescu–Hurlin (DH) panel causality test to examine the causality between spot electricity price and COVID‐19. The results reveal the unidirectional causality which is running from COVID‐19 to the spot electricity price discovery but no other way around. Our findings suggests to the policymakers that across different regions of India (North, East, West, South, and North‐East), the ongoing coronavirus outbreak will further disrupt the electricity market.