Electric Power Grid Research Articles

Zonal Machine Learning-Based Protection for Distribution Systems

Adaptive protection is defined as a real-time system that can modify the protective actions according to the changes in the system condition. An adaptive protection system (APS) is conventionally coordinated through a central management system located at the distribution system substation. An APS depends significantly on the communication infrastructure to monitor the latest status of the electric power grid and send appropriate settings to all of the protection relays existing in the grid. This makes an APS highly vulnerable to communication system failures (e.g., broken communication links due to natural disasters as well as wide-range cyber-attacks). To this end, this paper presents the addition of local adaptive modular protection (LAMP) units to the protection system to guarantee its reliable operation under extreme events when the operation of the APS is compromised. LAMP units operate in parallel with the conventional APS. As a backup, if APS fails to operate because of an issue in the communication system, LAMP units can accommodate a reliable fault detection and location on behalf of the protection relay. The performance of the proposed APS is verified using IEEE 123 node test system.

Design and Emulation of Physics-Centric Cyberattacks on an Electrical Power Transformer

Malware that attack the electrical power grid consist of exploits and operations modules. The exploits are similar to those of traditional malware. These malware hack into an industrial computer and subsequently deploy operational modules. Some operational modules penetrate the operating system of the compromised industrial computer to take over computing functions and hence facilitate further attacks. Examples include interception of cryptographic keys, and generation of deceptive status data that indicate normal operation of a power transformer, while in reality the transformer is in distress due to the attacks. Other operational modules are designed to recognize and disrupt the physics of the physical equipment. We refer to these operations modules as physics-centric modules. The subject of this research is how physics-centric modules of malware can cause physical damage to power grid equipment. This research simulates a power transformer and a set of its protection algorithms. We make several contributions in this research, namely: i) we emulate in Python the protection algorithms that run on an industrial computer and monitor and protect a power transformer from a variety of faults; ii) we leverage these emulations to analyze the cyberattack surface of a power transformer; iii) with these insights at hand, we devise attack modus operandi that malware could use against a power transformer; and iv) we emulate these cyberattacks in Python to empirically observe and quantify their destructive effects on a power transformer. Our overall research findings in this paper serve the purpose of informing better defense against the physics-centric modules of malware that attack the electrical power grid.

Study on the Impact of Renewable Energy Power Generation Transient Characteristics on System Stability in Weak Grid

With the large-mount access of renewable energy to the electric power grid, the unique seasonal and day and night output fluctuation of renewable energy power generation has brought new challenges to the stable and safe operation of the power system. These challenges mainly include the impact on power grid voltage stability, frequency stability and power angle stability. Therefore, the impact of renewable energy power generation on the stable and safe operation of power system should be be paid enough attention. Due to the characteristics of long-distance, large-scale and high concentration of grid connected renewable energy, the ability of AC system to connect to power grid is relatively weak, which is a typical weak current network. With the continuous expansion of renewable energy grid connection scale, the dynamic characteristics of weak current power grid become more complex, which brings great challenges to the stability and security of power grid. However, in the calculation of power system security and stability, more conservative renewable energy generation model parameters are often used to fix the power grid operation reliability. For better improve the renewable energy acceptance capacity of the system, the proportion of renewable power generation will be further increased. Therefore, establishing an accurate and reasonable renewable energy model is the basis of large-scale renewable energy grid connection security and stability calculation. Further study on the control strategy and parameter adaptability of renewable energy model could be reduced stability risk of high proportion of renewable energy connected to weak current network.

Short-Term Wind Energy Forecasting Using Deep Learning-Based Predictive Analytics

Wind energy is featured by instability due to a number of factors, such as weather, season, time of the day, climatic area and so on. Furthermore, instability in the generation of wind energy brings new challenges to electric power grids, such as reliability, flexibility, and power quality. This transition requires a plethora of advanced techniques for accurate forecasting of wind energy. In this context, wind energy forecasting is closely tied to machine learning (ML) and deep learning (DL) as emerging technologies to create an intelligent energy management paradigm. This article attempts to address the short-term wind energy forecasting problem in Estonia using a historical wind energy generation data set. Moreover, we taxonomically delve into the state-of-the-art ML and DL algorithms for wind energy forecasting and implement different trending ML and DL algorithms for the day-ahead forecast. For the selection of model parameters, a detailed exploratory data analysis is conducted. All models are trained on a real-time Estonian wind energy generation dataset for the first time with a frequency of 1 h. The main objective of the study is to foster an efficient forecasting technique for Estonia. The comparative analysis of the results indicates that Support Vector Machine (SVM), Non-linear Autoregressive Neural Networks (NAR), and Recurrent Neural Network-Long-Term Short-Term Memory (RNN-LSTM) are respectively 10%, 25%, and 32% more efficient compared to TSO's forecasting algorithm. Therefore, RNN-LSTM is the best-suited and computationally effective DL method for wind energy forecasting in Estonia and will serve as a futuristic solution.

Research and Implementation of Electric Topology Intelligent Checking System Based on Graph Computing

The information system of low-voltage distribution network correctly records the topological structure of distribution network, which is the premise of fine management and safe operation of electric power grid. In the topology, the most important element is the household change relationship [1]. At present, the low-voltage distribution network topology data in the information system is manually entered, so the correctness of the information can not be guaranteed. With the continuous expansion of the scale of power grid, the structure of low-voltage distribution network is complex, the amount of data of marketing measurement [2], GIS and other information systems is increasing rapidly, each information system operates independently, and the data circulation is poor, which makes it difficult to identify the topology data, so it is urgent to carry out topology verification. The method proposed in this paper effectively solves the problem of electric data verification, and carries out corresponding experiments and demonstrations on the results, with high accuracy.

A review of prosumers’ behaviours in smart grids and importance of smart grid management

Purpose: The concept of the smart grid is relatively new. The first aim of the study is to understand the behaviour of prosumers in smart grids. The other goal is to raise awareness of the management tasks and risks of smart grids by highlighting the relevant issues of some business networks (PPP projects, outsourcing, strategic alliances etc.). Methodology: Systemized literature review was used in the paper. Results: The discussed management problems of various business networks indicate that management challenges can also be expected in smart grids, so it is worth preparing in time. Conclusion: We found a lack of empirical research about the behaviour of prosumers and believe that studying the electric power grid of the future from a management perspective, that is, examining the possible behaviours and decisions of various actors, can provide valuable and useful information for smart grid design and safe operation insurance.

Water Wave Optimization with Deep Learning Driven Smart Grid Stability Prediction

Smart Grid (SG) technologies enable the acquisition of huge volumes of high dimension and multi-class data related to electric power grid operations through the integration of advanced metering infrastructures, control systems, and communication technologies. In SGs, user demand data is gathered and examined over the present supply criteria whereas the expenses are then informed to the clients so that they can decide about electricity consumption. Since the entire procedure is valued on the basis of time, it is essential to perform adaptive estimation of the SG’s stability. Recent advancements in Machine Learning (ML) and Deep Learning (DL) models enable the designing of effective stability prediction models in SGs. In this background, the current study introduces a novel Water Wave Optimization with Optimal Deep Learning Driven Smart Grid Stability Prediction (WWOODL-SGSP) model. The aim of the presented WWOODL-SGSP model is to predict the stability level of SGs in a proficient manner. To attain this, the proposed WWOODL-SGSP model initially applies normalization process to scale the data to a uniform level. Then, WWO algorithm is applied to choose an optimal subset of features from the pre-processed data. Next, Deep Belief Network (DBN) model is followed to predict the stability level of SGs. Finally, Slime Mold Algorithm (SMA) is exploited to fine tune the hyperparameters involved in DBN model. In order to validate the enhanced performance of the proposed WWOODL-SGSP model, a wide range of experimental analyses was performed. The simulation results confirm the enhanced predictive results of WWOODL-SGSP model over other recent approaches.

Smart Grid Technology Prospect of Implementation in Bangladesh

The term "smart grid" refers to the transformation of the traditional electric power grid into a modern grid. Modernization of the present electric power system is an important step to implement the Smart Grid technology. The structure of the existing power sector in Bangladesh is almost a hundred years old. Due to which the power sector of Bangladesh facing huge power wastage. A Smart Grid also ensures the efficient transmission and distribution of electric power. This paper gives a brief description of Smart Grid, the latest trends, challenges, prospects, cost analysis of smart grid equipment, and its facility. The spotlight of this paper is to implement the Smart Grid's perspective to Bangladesh. That’s included to have new distributed generation technology, smart meter, a pilot project, etc. Also, the main objective of this paper is the comprehensive development of transmission and distribution loss reduction, which will be saving a big amount of capital every year. And that will play a huge role in the economy of Bangladesh to move forward in global progress.

A Review on Power Electronics Technologies for Power Quality Improvement

Nowadays, new challenges arise relating to the compensation of power quality problems, where the introduction of innovative solutions based on power electronics is of paramount importance. The evolution from conventional electrical power grids to smart grids requires the use of a large number of power electronics converters, indispensable for the integration of key technologies, such as renewable energies, electric mobility and energy storage systems, which adds importance to power quality issues. Addressing these topics, this paper presents an extensive review on power electronics technologies applied to power quality improvement, highlighting, and explaining the main phenomena associated with the occurrence of power quality problems in smart grids, their cause and effects for different activity sectors, and the main power electronics topologies for each technological solution. More specifically, the paper presents a review and classification of the main power quality problems and the respective context with the standards, a review of power quality problems related to the power production from renewables, the contextualization with solid-state transformers, electric mobility and electrical railway systems, a review of power electronics solutions to compensate the main power quality problems, as well as power electronics solutions to guarantee high levels of power quality. Relevant experimental results and exemplificative developed power electronics prototypes are also presented throughout the paper.

Research and Solution Proposals to Optimize Distribution Power Grids in Smart Grid Condition

Smart Grid is a concept for transforming the electric power grid by using advanced automaticcontrol and communications techniques and other forms of information technology. It integratesinnovative tools and technologies from: generation, transmission and distribution. This also includesconsumer appliances and equipment. This concept integrates energy infrastructure, processes, devices,information and markets into a coordinated and collaborative process. All allowing energy to be generated,distributed and consumed flexibly and efficiently. However, the Smart Grid with the integration ofdistributed generation itself also creates a several disadvantages. There can be problems with: stabilityand reliability, relay protection, isolation and operational isolation in which the problem is to create aburden on the distribution grid when transmitting electrical energy sources. Optimizing power flow andbringing high operating efficiency on Smart Grid conditions is an urgent issue. This paper focuses onresearching and proposing solutions for optimal calculation of power flow on Smart Grid. The paper hasresearched, and analyzed calculation solutions to optimize power flow and proposed to use the Lagrangemultiplier method. The study performed calculations for a typical Smart Grid model with three distributedgenerations. Calculation results have shown that the role of the method is to fully perform the optimalcalculation of the power flow on the grid. This is in order to reduce power loss and energy loss as well asincreasing operational efficiency while improving power quality in Smart Grid conditions.

Classification of insulators using neural network based on computer vision

Insulators of the electrical power grid are usually installed outdoors, so they suffer from environmental stresses, such as the presence of contamination. Contamination can increase surface conductivity, which can lead to system failures, reducing the reliability of the network. The identification of insulators that have their properties compromised is important so that there are no discharges through its insulating body. To perform the classification of contaminated insulators, this paper presents computer vision techniques for the extraction of contamination characteristics, and a neural network (NN) model for the classification of this condition. Specifically, the Sobel edge detector, Canny edge detection, binarization with threshold, adaptive binarization with threshold, threshold with Otsu and Riddler–Calvard techniques will be evaluated. The results show that it is possible to have an accuracy of up to 97.50% for the classification of contaminated insulators from the extraction of characteristics with computer vision using the NN for the classification. The proposed model is more accurate than well-established models such as support-vector machine (SVM), k-nearest neighbor (k-NN), and ensemble learning methods. This showed that optimizing the model's parameters can make it superior to solve the problem in question.

Blockchain and Internet of Things for Electrical Energy Decentralization: A Review and System Architecture

The decentralization in the electrical power grids has gained increasing importance, especially in the last two decades, since transmission system operators (TSO), distribution system operators (DSO) and consumers are more aware of energy efficiency and energy sustainability issues. Therefore, globally, due to the introduction of energy production technologies near the consumers, in residential and industrial sectors, new scenarios of distributed energy resources (DER) are emerging. In order to guarantee an adequate power management in the electrical power grids, incorporating producers, consumers and producers-consumers (prosumers) together, it is important to adopt intelligent systems and platforms that allow the provision of information on energy consumption and production in real time, as well as for obtaining a fair price for the sale and purchase of energy. In this paper, we analyze the literature to identify the appropriate solutions to implement a decentralized electrical power grid based on sensors, blockchain and smart contracts, evaluating the current state of the art and pilot projects already in place. We also discuss a proposal for a power grid model, with renewable energy production, combining Internet of Things, blockchain and smart contracts.

Nexus-e: A platform of interfaced high-resolution models for energy-economic assessments of future electricity systems

Energy systems are transitioning toward sustainable power generation largely due to new policies that are motivated by climate and renewable generation targets. This transition is felt throughout the entire economy and is affecting the long and short term operations of the energy system. In the last decade, the research community has made significant efforts to model the energy transition and its impacts. Typically, these models focus on a limited subset of the different components of the energy-economic system, including investments in centralized and distributed generation, electricity markets, electric power grids, security of supply, and macro-economic effects. However, there are research questions that require the analysis of how these different aspects are connected to each other and how they may interact. Therefore, the research community has stressed the need to leverage models across domains to enhance the capabilities and robustness of their results. Due partly to the high complexity of combining models developed in tangential domains, few attempts have been made to model in full the interactions among the interdependent components of the energy-economic system. The Nexus-e: interconnected energy systems modeling platform aims to show how this gap can be filled by demonstrating that an interdisciplinary set of models can be integrated in a model framework by linking them through structured interfaces. This platform combines four bottom-up models that capture different aspects of the electricity system and one top-down macro-economic model to represent a much broader scope of the energy-economic system as compared to traditional stand-alone modeling approaches. In this paper, we study the benefits and limitations of the interfaces established among the modules in Nexus-e with reference to the Swiss electricity system in a European context. We demonstrate that prominent changes in operational behavior could drive investments and should therefore be assessed in a framework that considers both transmission and distribution systems in a coordinated manner. We show that the bottom-up investments and operations (transmission and distribution) can have an impact on the overall economy, which in response can affect the demand for electricity. Moreover, we demonstrate that the changes in generation mix and operation behaviors may have a significant impact on system security. For policymakers, the approach can provide spatially detailed power system transformation options that enable decisions that are socially, politically, and technically acceptable.

Measuring the balance of the world's largest machine

The electrical power grid has been described as the largest machine in the world. Although we customarily assume our electrical outlets provide a steady sinusoidal voltage with a frequency of 60 Hz (in North America), the variations from that ideal are easy to observe using the methods described in this article. These frequency variations are important since, as can be shown with introductory-level physics concepts, they arise when power generation and usage are not balanced on the electrical grid. The challenge of balancing the electrical grid will increase over the coming years as we adopt more sustainable but less predictable energy sources, and this connection to one of our current societal challenges makes this project interesting to students.

Advanced nonlinear controller of single‐phase shunt active power filter interfacing solar photovoltaic source and electrical power grid

This paper addresses the nonlinear controller design and the stability analysis of a shunt active power filter (SAPF) interfacing a solar photovoltaic (PV) system that consists of a couple of PV panels, a single-phase two-stage IGBT half-bridge SAPF and two identical DC link capacitors. We seek for the considered system the fulfillment of three control objectives: (a) extracting the maximum PV power using an appropriate maximum power point tracking (MPPT) algorithm; (b) regulation of the DC link capacitor voltages to the reference provided by the MPPT controller hence ensuring the exchange of power between the PV source and the AC power grid; (c) compensation for reactive power and undesired harmonics caused by the nonlinearity of electronic power loads, that is, performing power factor correction (PFC). The control objectives are achieved using a new two-loop cascaded controller. The Backstepping approach is applied in the inner loop to perform PFC. A filtered proportional-integral (PI) controller is applied in the outer loop to ensure tight regulation of the PV voltage. The MPPT control is implemented using a perturb-and-observe (PO) algorithm. The performance of the closed-loop system under the two-loop control strategy is formally analyzed through the Lyapunov approach applied to the averaged model. Numerical simulations are performed in MATLAB/SimPowerSystems environment to validate the design methodology and to confirm the theoretically predicted performances of the system under the proposed nonlinear controller.

Optimal amount of information determination for power system steady state estimation

On the basis of literature sources analysis, the paper provides the rationale for the necessity of considering the limited digital devices capabilities when designing closed digital control systems for the complex electrical power grids. The problem of design is decomposed into two subproblems: design of current state observation vector digital transmission systems and current controlled process state estimation; design of digital systems for optimal control vector calculation, transmission and control actions realization.The paper presents consideration of the former problem, i.e. design of current state observation vector digital transmission systems and current controlled process state estimation: the mathematical model of digital system of information transmission and state estimation considering speed and reliability of technical means of implementation is presented; the functional structure of simulation complex is developed; the paper provides the formulation of the problem of estimating the optimal amount of information about the control object state, resulting in a solution of computational experiments simulating complex.

IoT based smart and intelligent smart city energy optimization

With the effective result of IoT architecture in all research areas, we propose IoT framework based energy efficient smart and intelligent street road lighting system that consist of IoT sensor bases smart electric pole with controller for tuning LED lamps. In our work we replace traditional metal halide lamps with mesophic design LED lamps based on human eye sensitivity and it provides significant saving of energy too. Based on the sensor unit information about traffic flow and presence and absence of occupant smart and intelligent decision making module compute intensity level that generate different width pulse using PWM dimming system which triggers LED power switch via DALI controller mounted inside LED Light Controller. For efficient use of sustainable energy resources we use sustainable power systems that consist of PV solar panel units, Battery storage systems and smart electric power grids. For the charging battery system we use MPPT based dynamic battery charging algorithm. From experiment and simulated result, we observe that proposed energy efficient Smart Street lighting saves considerable amount of energy during peak and off peak hours not only at highway roads but at residential and sub-urban pedestrian areas also. It will finally decrease the consumption of energy and carbon emission. We perform comparative result analysis of proposed systems with existing lighting systems based on energy consumption. Our experimentation result also represents the effectiveness of the proposed dynamic battery charging algorithm based on the battery storage PV solar panel system performance.

Enhanced RSA key encryption application for metering data in smart grid

Purpose Digitizing of the electrical power grid promotes the advantages of efficient energy management alongside the possibilities of major vulnerabilities. A typical inadequacy that needs critical attention to ensure the seamless operation of the smart grid system remains in the data transmission between consumer premises smart devices and the utility centres. Many researches aim at establishing security protocols to ensure secure and efficient energy management resulting in perfect demand–supply balance. Design/methodology/approach In this paper, the authentication of the smart meter data has been proposed with enhanced Rivest–Shamir–Adleman (RSA) key encryption using an efficient way of generating large prime numbers. The trapdoor one-way function applied in the RSA algorithm makes it almost impossible for the reverse engineering attempts of cracking the key pair. Findings The algorithm for generating prime numbers has been tested both with the convention method and with the enhanced method of including a low-level primality test with a first few hundred primes. The combination of low-level and high-level primality tests shows an improvement in execution time of the algorithm. Originality/value There is a considerable improvement in the time complexities when using the combination method. This efficient generation of prime numbers can be successfully applied to the smart meter systems, thereby increasing the strength and speed of the key encryption.

Gray wolf optimization‐based optimal grid connected solar photovoltaic system with enhanced power quality features

AbstractIn recent times, regional renewable energy sources (RES) are increasingly integrated with the existing electric power grid by onboarding certain superior power quality features, thereby helping them in meeting massive electrical demand. This grid integration drastically reduces the use of fossil fuels, besides preventing environmental hazards as well. However, in grid‐connected systems, with RES such as photovoltaic (PV) systems and wind energy systems, many power quality issues (PQ) still crop up. In this paper, Gray Wolf Optimization (GWO), in conjunction with a static synchronous compensator (STATCOM) is suggested to address such challenges in the RES integrated grid‐connected PV systems. The intermittent nature of solar PV power under shading conditions can be harnessed by using the GWO approach. STATCOM is one of many specialized power electronic devices that effectively reduce power quality concerns such as voltage sag/swell and Total Harmonic Distortions. The fast‐acting PI controller is intended to drive a STATCOM by using some tuned parameters for maintaining the DC link voltage as constant. The best findings are attained after these results are compared with PI. The test system is modeled using the MATLAB/Simulink environment.

Upshot of unified power flow controller on the minimization of the severity of overloading on electric power grid

With the objective of discovering the vital line in an electrical power grid (EPG) where the unified power flow controller (UPFC) could be inserted to minimize the severity of overloading (SOL) during contingencies, an investigation into the stiffness of single line contingencies (SLC) on the EPG is presented in this study. An overloading index (OVI) is developed and used in this contribution to arrange the transmission branches of the EPG based on their cruciality and criticality throughout SLC. A harmony search algorithm (HSA), an optimization method, has been adopted to optimally set the parameters of the UPFC to achieve the best minimum SOL of the system at the optimal location. The IEEE 30-bus network was used as the test bed. Results based on the test bed show that placing a UPFC based on the ranking of a well-known contingency severity index (CSI) minimized the SOL to 2.40×1010 as against the 8.05×105 obtained when the placement is done on the ranking based on the proposed OVI. The minimization of SOL is achieved with a reactance and reactive power of 0.036pu and 0.096pu, respectively, of the UPFC. The result also reveals that the proposed OVI identifies with a higher level of precision the vital line in the test bed for placing the UPFC for the purpose of minimizing the SOL during contingencies.