Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions

Numerical simulation of electric signal in the cyber-physical immunosensor system on rectangular lattice in R package

Проведено чисельне моделювання електричного сигналу з перетворювача в кіберфізичній імуносенсорній системі на прямокутній решітці з використанням диференціальних рівнянь із запізненням за допомогою пакета R. Описано функціональні можливості пакета R як середовища програмування для статистичного аналізу даних, наведено корисні сайти, списки посилань і документація пакета R. У вигляді таблиці представлено назви параметрів моделі імуносенсора на прямокутній решітці з використанням диференціальних рівнянь із запізненням та їх числові значення в пакеті R. Реалізовано комп’ютерну програму «Чисельний аналіз електричного сигналу з перетворювача, який характеризує кількість флуоресціюючих пікселів в імуносенсорі на прямокутній решітці з використанням диференціальних рівнянь із запізненням». Розроблена комп’ютерна програма дає змогу провести дослідження стійкості імуносенсорних систем, які широко використовуються для отримання діагностичної інформації з метою оцінювання критичних станів при серцево-судинних захворюваннях, величини інсуліну при вимірюванні величини глюкози в крові та виявлення кількісних показників у деяких фармацевтичних сполуках. Наведено фрагмент лістингу комп’ютерної програми в пакеті R для отримання електричного сигналу з перетворювача, який характеризує кількість флуоресціюючих пікселів у кіберфізичній імуносенсорній системі на прямокутній решітці з використанням диференціальних рівнянь із запізненням. Проведено чисельне моделювання для електричного сигналу з перетворювача в імуносенсорі на прямокутній решітці з використанням диференціальних рівнянь із запізненням. Проаналізовано зміни отриманого електричного сигналу, які відповідають кількості флуоресціюючих пікселів у кіберфізичній імуносенсорній системі. Обгрунтовано використання пакета R як вільнопоширюваного програмного забезпечення з графічною візуалізацією результатів аналізу.The numerical simulation of electric signal from the converter in the cyber-physical immunosensor system on rectangular lattice using differential equations with delay by means of R package is carried out in this paper. The functional features of R package as a programming environment for statistical data analysis are described, useful sites, references lists and documentation of R package are given. The names of parameters of the immunosensor model on rectangular lattice using the differential equations with delay and their numerical values in the package R are presented in the form of the table. The computer program «Numerical analysis of the electrical signal from the converter that characterizes the number of fluorescing pixels in the immunosensor on rectangular lattice using delayed differential equations» is implemented. The developed computer program makes it possible to carry out the investigation of the stability of immunosensory systems, which are widely used to obtain diagnostic information in order to evaluate critical states of cardiovascular disease, insulin values while measuring blood glucose values and identify quantitative indicators in some рharmaceutics compounds. The fragment of computer program listing in R package for obtaining the electrical signal from converter characterizing the number of fluorescent pixels in cyber-physical immunosensor system on rectangular lattice using delayed differential equations is presented. Numerical simulation for the electric signal from the converter in the immunosensor on rectangular lattice using the delayed differential equations is carried out. The changes of the received electrical signal corresponding to the number of fluorescent pixels in the cyber-physical immunosensory system are analyzed. The use of R package as a freely distributed software with graphical visualization of the analysis results is substantiated

Cyber Physical Energy Systems Modules for Power Sharing Controllers in Inverter Based Microgrids

The Microgrids (MGs) are an effective way to deal with the smart grid challenges, including service continuity in the event of a grid interruption, and renewable energy integration. The MGs are compounded by multiple distributed generators (DGs), and the main control goals are load demand sharing and voltage and frequency stability. Important research has been reported to cope with the implementation challenges of the MGs including the power sharing control problem, where the use of cybernetic components such as virtual components, and communication systems is a common characteristic. The use of these cybernetic components to control complex physical systems generates new modeling challenges in order to achieve an adequate balance between complexity and accuracy in the MG model. The standardization problem of the cyber-physical MG models is addressed in this work, using a cyber-physical energy systems (CPES) modeling methodology to build integrated modules, and define the communication architectures that each power sharing control strategy requires in an AC-MG. Based on these modules, the control designer can identify the signals and components that eventually require a time delay analysis, communication requirements evaluation, and cyber-attacks’ prevention strategies. Similarly, the modules of each strategy allow for analyzing the potential advantages and drawbacks of each power sharing control technique from a cyber physical perspective

Future Perspectives of Co-Simulation in the Smart Grid Domain

The recent attention towards research and development in cyber-physical energy systems has introduced the necessity of emerging multi-domain co-simulation tools. Different educational, research and industrial efforts have been set to tackle the co-simulation topic from several perspectives. The majority of previous works has addressed the standardization of models and interfaces for data exchange, automation of simulation, as well as improving performance and accuracy of co-simulation setups. Furthermore, the domains of interest so far have involved communication, control, markets and the environment in addition to physical energy systems. However, the current characteristics and state of co-simulation testbeds need to be re-evaluated for future research demands. These demands vary from new domains of interest, such as human and social behavior models, to new applications of co-simulation, such as holistic prognosis and system planning. This paper aims to formulate these research demands that can then be used as a road map and guideline for future development of co-simulation in cyber-physical energy systems

Artificial Neuron-Based Model for a Hybrid Real-Time System: Induction Motor Case Study

Automatic Machine Learning (AML) methods are currently considered of great interest for use in the development of cyber-physical systems. However, in practice, they present serious application problems with respect to fitness computation, overfitting, lack of scalability, and the need for an enormous amount of time for the computation of neural network hyperparameters. In this work, we have experimentally investigated the impact of continuous updating and validation of the hyperparameters, on the performance of a cyber-physical model, with four estimators based on feedforward and narx ANNs, all with the gradient descent-based optimization technique. The main objective is to demonstrate that the optimized values of the hyperparameters can be validated by simulation with MATLAB/Simulink following a mixed approach based on interleaving the updates of their values with a classical training of the ANNs without affecting their efficiency and automaticity of the proposed method. For the two relevant variables of an Induction Motor (IM), two sets of estimators have been trained from the input current and voltage data. In contrast, the training data for the speed and output electromagnetic torque of the IM have been established with the help of a new Simulink model developed entirely. The results have demonstrated the effectiveness of ANN estimators obtained with the Deep Learning Toolbox (DLT) that we used to transform the trained ANNs into blocks that can be directly used in cyber-physical models designed with Simulink.Junta de Andalucia B-TIC-42-UGR20European CommissionSpanish Science Ministry (Ministerio de Ciencia e Innovacion) PID2020-112495RB-C2