WEB-ENABLED GENERALIZED ARCHITECTURAL MODEL FOR ONLINE POWER SYSTEM ANALYSIS

ABSTRACTThe novelty behind this research is to develop enhanced models for real‐time power system monitoring in a platform‐independent and distributed platform. Distributed models for power system applications have been implemented using Remote Method Invocation (RMI), Common object Request Broker Architecture and. NET remoting technologies. The interoperability among the heterogeneous power system clients in a distributed platform has been achieved through XMLization of power system data which provides meaningful solution to the legacy issues associated with power systems. A RMI‐based model for real‐time power system monitoring in a distributed platform had been implemented and discussed in detail. A component model for real‐time power system monitoring has also been proposed and implemented to increase the reusability and scalability of the system. A location transparent model has been developed using Common object Request Broker Architecture with RMI‐Internet Inter‐Orb Protocol for load flow monitoring and the idea is extended to solve real‐time economic load dispatch and dynamic security monitoring. The detailed monitoring on performance of the proposed models has been done with load flow monitoring, dynamic security monitoring and economic load dispatch problems. A. NET remoting architectural model has been developed for real‐time economic load dispatch in a distributed platform. This research paper explores various issues in deploying power system applications in a distributed platform. It will serve the purpose of stimulating interest among power engineers and also will be extremely useful for electric power utilities.

Nowadays, the increasing energy demand, development of smart grids, and the combination of different energy systems have led to the complexity of power systems. On the other hand, ever-expanding energy consumption, development of industry and technology systems, and high penetration of solar and wind energies have made electricity networks operate in more complex and uncertain conditions. Therefore, analysis of traditional power and energy systems requires physical modeling and extensive numerical computation. To analyze these systems’ behavior, advanced metering and condition monitoring devices and systems are utilized, which generate huge amounts of data. Assessment of these data is approximately impossible by conventional methods and requires powerful data mining procedures. Machine learning, deep learning, and a variety of regression, classification, and clustering algorithms are powerful tools to use in these issues. These procedures can be utilized for load/demand forecasting, demand response evaluation, defect/fault detection in electrical equipment, power system analysis and control, cybersecurity, and renewable energy generation prediction. Understanding the structure and functioning of each learning method is therefore one of the most important issues in the application of them to solve power system problems. In this chapter, we will introduce and discuss selected methods of data mining based on their learning, structure, formulation, mode of operation, and application in power systems. Literature on machine learning and deep learning procedures, train and test process of networked methods, and, finally, applications of each procedure are presented in this chapter.

A memory output sliding mode load frequency control (MOSMLFC) strategy is proposed for multi-area interconnected power systems under historical-frequency-triggered denial-of-service (DoS) attacks. Due to the use of the open network, the multi-area power system is prone to cyber-attacks. Different types of attack models have been built to describe the actual attack behavior, so that effective strategies can be quickly formulated in the event of an attack. Therefore, a historical-frequency-triggered DoS attacks model is presented from the perspective of attackers, with the aim of destroying the stable state of the multi-area power system. It is assumed that attackers determine the timing of DoS attacks by monitoring the operational status of multi-area power systems and designing the triggering condition with historical frequency. A MOSMLFC strategy is investigated to ensure the security performance of multi-area power systems under historical-frequency-triggered DoS attacks, which applies the memory output information of the power system to realize the controller design. The security condition of multi-area power systems under historical-frequency-triggered DoS attacks is obtained by Lyapunov’s theorem and linear matrix inequality (LMI). Numerical examples are tested over the IEEE 10-generator 39-bus system and the results prove the usefulness and superiority of the proposed method. Note to Practitioners—Load frequency control is widely applied in multi-area power systems to achieve a balance between the load demand and generation. Frequent cyber-attacks are a threat to the normal operation of the power system. It is therefore necessary to develop appropriate strategies to defend against cyber-attacks. So far, there have been many different forms of cyber-attacks. This has prompted defenders to build different types of attack models to describe the actual attack behavior in order to preemptively formulate appropriate defensive strategies. Smart attacker may notice that certain characteristics of the target system are important, such as the power system frequency. This motivates us to propose a historical frequency-triggered DoS attack model that contributes to a deep understanding of the impact of cyber-attacks on the power system. We propose a unique sliding mode control approach to ensure the stable performance of power system state and output simultaneously.

Nowadays, the increase in electrical energy consumption and power system restructuring have posed new challenges to the operation, control, and monitoring of power systems. In this situation, the supervisory control and data acquisition (SCADA) system is not enough to ensure power system security and stability. The SCADA is often unable to measure data of all buses simultaneously. In addition, the sampling rate in this system is not enough for some power system applications. Therefore, the information obtained from SCADA does not show power system dynamics properly. In order to improve the power system monitoring, wide area measurement system (WAMS) has been developed to overcome the problems of SCADA system. Phasor measurements units (PMUs) are the main part of WAMS system and it basically consists of three essential processes including collecting, transmitting, and analyzing data. WAMS receives obtained data via a high speed communication links. After data processing and extracting appropriate information, decisions are made to improve the power system performance. Efficient use of power system data to achieve a secure operation strategy is targeted using the WAMS system. Due to the effective role of WAMS and PMUs in the reliable operation of power system, it is necessary to study their concept and applications in this chapter. The history of PMU and its structure is presented in this chapter. In addition, the necessity of WAMS for future power system and its difference from SCADA system have been investigated in this chapter. Different algorithms and application of the WAMS are also introduced in this chapter which can be implemented to improve the performance of the future power system.

Although the standards-based systems integration of utility software applications using the domain data standard common information model (CIM) exists, many utilities are facing difficulties in managing of data exchanges among software application systems due to lack of standard oriented database management systems. The standard oriented database can be built from the CIM object oriented model using object-relational mapping (ORM). The generic design of ORM can handle object oriented model mapping to any database schema semantics. This paper presents a CIM oriented database (CIMODB) design through the object-relational mapping (ORM). The flexibility of data moving between object-oriented paradigms and relational database paradigms is achieved with the use of ORM concepts. Furthermore, the paper explores the integration of upstream power system applications with the proposed CIMODB, such as, topology processing, load-flow analysis, and more power system studies at the control center level from the legacy vendor software systems. A case study is illustrated to elaborate the CIMODB concept on the 400kV 20-substation Maharashtra network, which is the part of western region in India.

WAMS will play a vital role in the monitoring, analysis, modeling and control of interconnected power system. However, the impact of time delays introduced by WAMS can’t be ignored. A novel framework called the Cluster Treatment of Characteristic Roots (CTCR) is introduced to analyze the stability of a power system considering the feedback transmission delays. The “Building Block” concept yields a very practical and efficient procedure for the stability assessment of multiple time delayed systems. This novel perspective serves very well for the preparatory steps of the CTCR method. The complete stability picture of a multiple time delayed Single-Machine Infinite Bus (SMIB) system is obtained through the combination of “Building Block” concept and the CTCR method in time delay space. The validity of this method is verified by time domain simulation results.

Foreword . Preface . Acknowledgements . 1 Energy and Electricity . 1.1 The World Energy Scene. 1.2 The Environmental Impact of Energy Use. 1.3 Generating Electricity. 1.4 The Electrical Power System. References. 2 Features of Conventional and Renewable Generation . 2.1 Introduction. 2.2 Conventional Sources: Coal, Gas and Nuclear. 2.3 Hydroelectric Power. 2.4 Wind Power. 2.5 PV and Solar Thermal Electricity. 2.6 Tidal Power. 2.7 Wave Power. 2.8 Biomass. 2.9 Summary of Power Generation Characteristics. 2.10 Combining Sources. References. 3 Power Balance/ Frequency Control . 3.1 Introduction. 3.2 Electricity Demand. 3.3 Power Governing. 3.4 Dynamic Frequency Control of Large Systems. 3.5 Impact of Renewable Generation on Frequency Control and Reliability. 3.6 Frequency Response Services from Renewables. 3.7 Frequency Control Modelling. 3.8 Energy Storage. References. Other Useful Reading. 4 Electrical Power Generation and Conditioning . 4.1 The Conversion of Renewable Energy into Electrical Form. 4.2 The Synchronous Generator. 4.3 The Transformer. 4.4 The Asynchronous Generator. 4.5 Power Electronics. 4.6 Applications to Renewable Energy Generators. References. 5 Power System Analysis . 5.1 Introduction. 5.2 The Transmission System. 5.3 Voltage Control. 5.4 Power Flow in an Individual Section of Line. 5.5 Reactive Power Management. 5.6 Load Flow and Power System Simulation. 5.7 Faults and Protection. 5.8 Time Varying and Dynamic Simulations. 5.9 Reliability Analysis. References. 6 Renewable Energy Generation in Power Systems . 6.1 Distributed Generation. 6.2 Voltage Effects. 6.3 Thermal Limits. 6.4 Other Embedded Generation Issues. 6.5 Islanding. 6.6 Fault Ride-through. 6.7 Generator and Converter Characteristics. References. 7 Power System Economics and the Electricity Market . 7.1 Introduction. 7.2 The Costs of Electricity Generation. 7.3 Economic Optimization in Power Systems. 7.4 External Costs. 7.5 Effects of Embedded Generation. 7.6 Support Mechanisms for Renewable Energy. 7.7 Electricity Trading. References. 8 The Future - Towards a Sustainable Electricity Supply System . 8.1 Introduction. 8.2 The Future of Wind Power. 8.3 The Future of Solar Power. 8.4 The Future of Biofuels. 8.5 The Future of Hydro and Marine Power. 8.6 Distributed Generation and the Shape of Future Networks. 8.7 Conclusions. References. Appendix: Basic Electric Power Engineering Concepts . A.1 Introduction. A.2 Generators and Consumers of Energy. A.3 Why AC?. A.4 AC Waveforms. A.5 Response of Circuit Components to AC. A.6 Phasors. A.7 Phasor Addition. A.8 Rectangular Notation. A.9 Reactance and Impedance. A.10 Power in AC Circuits. A.11 Reactive Power. A.12 Complex Power. A.13 Conservation of Active and Reactive Power. A.14 Effects of Reactive Power Flow - Power Factor Correction. A.15 Three-phase AC. A.16 The Thevenin Equivalent Circuit. Reference. Index.

Though CIM brings an integration of proprietary software applications of a utility, managing a sheer volume of data between the software application systems is still challenging for utility operators. With the advent of high performance database technologies and real-time big-data processing tools available, CIM oriented database can play important role in the operation and processing of the power system data. As the power system resembles connected-data, the choice of the database technology has to be based on the performance measures such as high-speed data retrieval of the connected-data and efficient storage. This paper presents a CIM oriented graph database (CIMGDB) by the object-graph mapping methodology. The integration of the CIMGDB with power system applications is discussed by the developed implementation architecture. The network topology processing (NTP) application is implemented on the CIMGDB. The NTP is tested on the six IEEE test systems and on one practical power system network. The six IEEE test systems are considered in evaluating and comparing the time complexity of CIMGDB with the CIM oriented relational database framework. The practical power system network is considered to demonstrate the implementation architecture and the CIMGDB integration with power system applications.

AIn this paper, impact of communication delay on distributed load frequency control (dis-LFC) of multi-area interconnected power system (MAIPS) is investigated. Load frequency control (LFC), as one of ancillary services, is aimed at maintaining system frequency and inter-area tie-line power close to the scheduled values, by load reference set-point manipulation and consideration of the system constraints. Centralized LFC (cen-LFC) requires inherent communication bandwidth limitations, stability and computational complexity, as such, it is not a good technique for the control of large-scale and geographically wide power systems. To decrease the system dimensionality and increase performance efficiency, distributed and decentralized control techniques are adopted. In distributed LFC (dis-LFC) of MAIPS, each control area (CA) is equipped with a local controller and are made to exchange their control actions by communication with controllers in the neighboring areas. The delay in this communication can affect the performance of the LFC scheme and in a worst case deteriorates power system stability. To investigate the impact of this delay, model predictive controller (MPC) is employed in the presence of constraints and external disturbances to serve as LFC tracking control. The scheme discretizes the system and solves an on-line optimization at each time sample. The system is subjected to communication delay between the CAs, and the response to the step load perturbation with and without the delay. Time-based simulations were used on a three-area MAIPS in MATLAB/SIMULINK environment to verify the investigations. The overshoot and settling time in the results reveals deterioration of the control performance with delay. Also, the dis-LFC led to zero steady states errors for frequency deviations and enhanced the MAIPS’ performance. With this achievement, MPC proved its constraints handling capability, online rolling optimization and ability to predict future behavior of systems.

Because of uncertainties of renewable generation resources and load, efficient tools are required for load flow analysis in power systems. Many of the existing papers assume a set of given probability density functions (PDFs) to model uncertainties and develop parametric probabilistic load flow tools. However, the uncertainties might not fall in any standard class of PDFs. Thus, nonparametric tools are needed. This paper presents a method for nonparametric probabilistic load flow analysis to determine PDFs of the load flow outputs. The proposed method is based on the mean value first order saddle point approximation. For a system with $\boldsymbol {n}$ random variables, $\boldsymbol {(n+1)}$ load flow calculations are utilized to establish first order Taylor series expansion and then saddle point approximation is adopted to determine the probabilistic characteristic of desired output variables. The proposed nonparametric estimator provides accurate results while needing a reasonable computational effort. The estimator needs only a limited available data set of random variables without requiring information whether the data set belongs to a certain class of parametric distribution functions. Furthermore, both probability and cumulative distribution functions of load flow outputs are directly established without using integral or differential operators. The proposed method is tested on the IEEE 14-bus and the IEEE 118-bus test systems and promising results are obtained.

The world's modernization resulted to a sharp rise in the demand for power. Decentralized power generation and the inclusion of renewable energy sources are making power system operation more complex. As a result, power system monitoring and measurement also became more complicated. One of the main disadvantages of power system monitoring is the slower rate of the measurement and monitoring data obtained from the current power system monitoring techniques, such as Supervisory Control and Data Acquisition (SCADA) and Energy Management Systems (EMS), as they only provide a limited amount of information on the parameters of the power system. Therefore, it is necessary to improve the current power system monitoring techniques for gathering data from the power system. Phasor measurements using Intelligent Electronic Devices (IEDs) provide faster data gathering from the power system and more information about the parameters of the power system. In order to determine the phasor values of voltage, current, remaining power system operational parameters frequency, Change of Frequency (COF) and Rate of Change of Frequency (ROCOF), this IED gathers the voltage and current signal data from the power system more quickly and sends it to a software program. By using this software program, very quickly phsors, power system operational parameters are calculated.The calculated phasors gave the live representation of the power system feasible by appending a time tag to each phasor value. This is a crucial requirement for power system monitoring across a large area, leads to massive storage system of phasors and power system paprameters. To store only required and effective data and to avoid routine data storage, new storage method is proposed. This paper presents a phasor calculation technique, synchronized phasor measurements, effective data storage, and identifying of faults in the dynamic state.

This paper presents design of a generic sparse matrix model for a class of large sparse linear system over real and complex numbers. Such matrices arise in various power system analysis applications like load flow analysis and short circuit analysis. Template facility of C++ is used to write a generic program on float, double and complex data types. Design of specific algorithm optimization for different power system application modules guarantees numerical stability and efficient sparsity implementation. Graph theory and class are introduced to facilitate and simplify the factorization of sparse matrix. The proposed sparse matrix model reflects an important development toward a truly object oriented power system analysis software.

Synchronized Phasor Measurement Units (PMUs) are becoming a key element of monitoring, protection, control and state estimation applications in power systems and are the basis for the implementation of Wide Area Monitoring Systems (WAMSs). This paper investigates the possibility of using a software-only PTP (Precision Time Protocol) synchronization method to synchronize different Phasor Measurement Units (PMUs) and how this synchronization impacts the State Estimation. In this context, as a case study, the output data of the PMUs have been used to estimate the state of a power system of reduced size and complexity. A laboratory experimental setup has been built to evaluate the performance achievable with the proposed synchronization solution. The results thus obtained and the performance analysis are discussed in this paper.

This paper presents a new decomposition and coordination algorithm based on Ward equivalent to solve distributed reactive-power optimization problem in multi-area power system. At first, multi-area interconnected power system is partitioned into master and slaver areas, boundary buses are defined as different bus types respectively corresponding to different areas, and hence the transfer method of phase angles from master area to slaver areas is determined. Secondly the initial value of equivalent injection power is obtained according to power-flow calculation at basic operating state. Then computational efficiency of the equivalent admittance matrix of external network due to change of transformer ratios is highly enhanced during optimization process by means of the inverse matrix modification lemma, and also the equivalent accuracy of external network is improved greatly. Moreover a kind of external coordination strategy which is suitable for reactive-power optimization calculation is introduced and better coordination effects with less coordination communications are obtained. Finally testing results on 538-bus practical systems is used to validate the correctness and effectiveness of the proposed algorithm in comparison with the centralized optimization method.

This book provides a compact yet comprehensive treatment of advanced data-driven signal processing techniques for the analysis and characterization of both ambient power system data and transient oscillations resulting from major disturbances. Inspired by recent developments in multi-sensor data fusion, multi-temporal data assimilation techniques for power system monitoring are proposed and tested in the context of modern wide-area monitoring system architectures. Recent advances in understanding and modeling nonlinear, time-varying power system processes are reviewed and factors affecting the performance these techniques are discussed. A number of algorithms and examples are presented throughout the text as an aid to understanding the basic material provided. Challenges involved in realistic monitoring, visualization and analysis of actual disturbance events are emphasized and examples of applications to a wide range of power networks are provided. Topics covered include: wide-area monitoring and analysis systems; wide-area monitoring system architectures; spatio-temporal modeling of power system dynamic processes; advanced data processing and feature extraction; multi-sensor multitemporal data fusion; monitoring the status of the system; near real-time analysis and monitoring; and interpretation and visualization of wide-area PMU measurements. This book will be of interest to students, researchers and engineers involved in the study, design, analysis and control of power systems