Fast Dynamic Simulation-Based Small Signal Stability Assessment and Control

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.

It is very important to take into consideration time delay in wide area power system stability; the design of PSS (Power System Stabilizer) should consider global optimization with WAMS (Wide Area Measurement System) time delay. Newly designed PSS should be insensitive to time delay and suppress internal low frequency oscillations. It is used as feedback signal and is real-time synchronous that WAMS signal shows. Power system is modeled with the consideration of time delay. LMI (Linear Matrix Inequalities) is used to solve the stability condition of time delay system. Based on the time-delay effect of the wide-area measurement signals, this paper redesigned the PSS with global optimization of power system. The attached two-area-four-machine system simulation illustrates that wide-area PSS designed by global optimization with the consideration of time-delay can limit internal low frequency oscillation with time-delay insensitivity, and improve the stability of power system. It implements global optimization of PSS with WAMS time delay stability.

This paper presents techniques for the application of tertiary and secondary voltage control through the use of intelligent proportional integral derivative (PID) controllers and the wide area measurement system (WAMS) in the IEEE 39 bus system (New England system). The paper includes power system partitioning, pilot bus selection, phasor measurement unit (PMU) placement, and optimal secondary voltage control parameter calculations to enable the application of the proposed voltage control. The power system simulation and analyses were performed using the DIgSILENT and MATLAB software applications. The optimal PMU placement was performed in order to apply secondary voltage control. The tertiary voltage control was performed through an optimal power flow optimization process in order to minimize the active power losses. Two different methods were used to design the PID secondary voltage control, namely, genetic algorithm (GA) and neural network based on genetic algorithm (NNGA). A comparison of system performances using these two methods under different operating conditions is presented. The results show that NNGA secondary PID controllers are more robust than GA ones. The paper also presents a comparison between system performance with and without secondary voltage control, in terms of voltage deviation index and total active power losses. The graph theory is used in system partitioning, and sensitivity analysis is used in pilot bus selection, the results of which proved their effectiveness.

Models of different parts of the DC power system including the energy storage devices—supercapacitors (SCs) are discussed in this paper. Charging and discharging voltage thresholds and SOC (State of Charge) should be taken into consideration when establishing the SCs model. Also the algorithm to calculate the power flow of the system changes after the addition of SCs. Combined with the states of traction network, the control strategy of SCs is analyzed in detail. The dynamic simulation is based on the Chain—rule Reduction of Ladder Circuit Jacobian Matrices method. To compare the energy saving and voltage stabilization effects, three equipment installation conditions—with braking resistors (BRs) only, with SCs only, with neither of them, are set in this paper. The simulation program is edited in Matlab with m language. Finally with this program the simulation results of a certain metro line in three conditions were compared and analyzed, and all effects were achieved.

Protection of power systems is important for stable and uninterrupted operation of power network. Maloperation of traditional back up protection schemes (e.g-Zone3 maloperation) causes many catastrophic phenomenon like cascaded tripping in power systems. So, some defensive mechanism are needed for taking care of such failures in power systems and to increase their security and reliability. Due to hugeness and complexity of Modern power systems, they are controlled, monitored and protected by Wide Area Measurement systems (WAMS). WAMS increases the efficiency and reliability of power networks and prevent the chance of blackouts and cascading failures in tandem. A synchronized phasor measurement-based wide-area backup protection scheme is presented in the paper where the magnitude of sequence voltages of buses at a system protection centre are used to identify the faulted area, bus closest to the fault and faulted line. The proposed method is tested for several faults including simultaneous faults in power systems with interconnections also. This technique is found to be Reliable, accurate and fast with contemporary synchronized measurement based technology. It is expected that numerous disastrous blackouts, cascaded tripping of power networks can be avoided and reliability and security of the power systems can be improved by the scheme. The study of new and improved back up protection scheme is done on a WSCC-3 machine-9 bus system. The data is simulated through EMTDC/PSCAD and MATLAB /SIMULINK software.

In order to easily monitor the wide-area power system information covering many power producers, suppliers, and power companies, authors have been developed a PMU (phasor measurement unit) based WAMS (wide area measurement system). This WAMS momentarily gather information about the power oscillation affecting the stability of Japanese longitudinal power systems by signal processing of the power outlet phasor. This paper introduces the features of the PMU based WAMS, and an availability of the power outlet phasor is validated by comparing the measurements with actual power station. And as one of the applications, an estimation method of interconnected power system parameters from the monitored power oscillation information is proposed.

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.

This paper presentsH∞excitation control design problem for power systems with input time delay and disturbances by using nonlinear Hamiltonian system theory. The impact of time delays introduced by remote signal transmission and processing in wide-area measurement system (WAMS) is well considered. Meanwhile, the systems under investigation are disturbed by random fluctuation. First, under prefeedback technique, the power systems are described as a nonlinear Hamiltonian system. Then theH∞excitation controller of generators connected to distant power systems with time delay and stochasticity is designed. Based on Lyapunov functional method, some sufficient conditions are proposed to guarantee the rationality and validity of the proposed control law. The closed-loop systems under the control law are asymptotically stable in mean square independent of the time delay. And we through a simulation of a two-machine power system prove the effectiveness of the results proposed in this paper.

Guest Editorial: Situational awareness of integrated energy systems

This study investigated the spatial distributions and long-term trends of the annual highest and lowest temperatures (summer peak temperature, SPT; winter peak temperature, WPT) and their timings (summer peak day, SPD; winter peak day, WPD) in South Korea and analyzed their relationship with the general circulation patterns. The two peaks were determined by selecting the highest and the lowest points after extracting temperature variations longer than the seasonal scale (91 days) in the time series of daily mean temperatures. For the long-term trend, we examined data for 100 years (1909–2008) for five stations and data for 35 years (1974–2008) for 61 stations. The SPD in South Korea is August 4 on average. It is earliest (July 31) in the central inland region, the central hilly sections, and the southern inland region and latest (August 10) in the southern coastal region and on Jeju Island (Seogwipo). The WPD in South Korea is January 16 on average. It is earliest (January 13) in the central inland region and southern inland region and latest (January 24) on Jeju Island (Jeju) and in Ulleungdo. The SPT and WPT are highest on Jeju Island (Seogwipo; 27.3 and 6.4 °C, respectively) and lowest in the central hilly sections (Daegwallyeong; 20.2 and −7.9 °C, respectively). The interannual variations in the WPD and WPT are greater than those in SPD and SPT. A significant increasing trend in the WPT was observed for all of the analyzed stations only for the second half of the 100-year period (1959–2008). In the case of the 35-year period, the SPD did not show any clear changes at all stations, but the WPD tended to occur earlier at three stations in the east coastal area. The WPT showed an increasing trend at 55 stations for 35 years, but the SPT showed an increasing trend only in Seogwipo (0.041 °C/year) and even showed a decreasing trend in Mungyeong (−0.049 °C/year). General circulation patterns were indexed and their correlations with the seasonal peaks were investigated. No correlations were found with the SPD. However, the WPD showed a significant positive correlation with the day of the highest Siberian High Intensity (SHI) and the day of the lowest Arctic Oscillation Index (AOI). Furthermore, the SPT showed negative correlations with the intensities of the Okhotsk High and North Pacific High, whereas the WPT showed a negative correlation with SHI and a positive correlation with AOI and with the intensity of the northerly wind that flows into the Korean Peninsula.

The ability of data recovery during communicational failures is one of the significant advantages of the wide area measurement system (WAMS) with hierarchical structure. For this purpose, in addition to the proper partitioning of the WAMS, the relative location of phasor measurement units (PMUs) and phasor data concentrators (PDCs) should be selected so as to maximize the reliability of the communication network. The aim of this paper is to develop an organized method for partitioning the WAMS as well as proposing a new algorithm for the simultaneous optimal placement of PMUs and PDCs. In the proposed method, first it is assumed that PMUs are installed at all of the system buses. Then, redundant PMUs will be omitted sequentially to provide the system observability with the maximum communication reliability. The IEEE 30-, and 118-bus systems are used to demonstrate the proposed algorithm. Contingencies such as PMU loss and line outage are considered in power system observability achievement. Finally, to show the applicability of proposed methods to real dimensional power systems, the IEEE 300-bus power system is also analyzed in the normal operating condition.

The role of seasonal variation in peritoneal dialysis (PD)-related peritonitis has been limited to a few small single-centre studies. Using all 6610 Australian patients receiving PD between 1 October 2003 and 31 December 2008, we evaluated the influence of seasons on peritonitis rates (Poisson regression) and outcomes (multivariable logistic regression). The overall rate of peritonitis was 0.59 episodes per patient-year of treatment. Using winter as the reference season, the peritonitis incidence rate ratios (95% confidence interval) for summer, autumn and spring were 1.02 (0.95-1.09), 1.01 (0.94-1.08) and 0.99 (0.92-1.06), respectively. Significant seasonal variations were observed in the rates of peritonitis caused by coagulase-negative Staphylococci (spring and summer peaks), corynebacteria (winter peak) and Gram-negative organisms (summer and autumn peaks). There were trends to seasonal variations in fungal peritonitis (summer and autumn peaks) and pseudomonas peritonitis (summer peak). No significant seasonal variations were observed for other organisms. Peritonitis outcomes did not significantly vary according to season. Seasonal variation has no appreciable influence on overall PD peritonitis rates or clinical outcomes. Nevertheless, significant seasonal variations were observed in the rates of peritonitis due to specific microorganisms, which may allow institutions to more precisely target infection control strategies prior to higher risk seasons.

With the wide application of synchronized phase measurement unit in power system, the wide-area measurement (WAM) system has enabled the use of a combination of measured information from remote location for improving transient stability. The paper focuses on a two-level hierarchical decentralized coordinated excitation control consists of multiple decentralized local fuzzy power system stabilizers (LFPSS) for each generator at the first level helped by a WAM based supervisory power system stabilizer (SPSS) at the secondary level for wide-area power system transient stability enhancement. In order to eliminate the inherent nonlinearities in the power system model and enable the model to be applicable to power system control, first, a direct feedback linearization compensator acting through the local excitation machine is proposed. Then, the T-S fuzzy model is employed to approximate the system model. Based on the T-S fuzzy model, the LFPSS is designed. And the SPSS is proposed to apply the remote signals from the WAM systems. For taking consideration of the time delays introduced by remote information’s transmission in WAM systems and overcoming their impacts on control performance, the authors develop a delay-independent H∞ robust control technique. Some new robust stability criteria in terms of LMI are derived by Lyapunov stability theory incorporating LMI techniques. Simulation examples demonstrate that the decentralized coordinated robust control has better transient stability performance in the face of transmission time delays, severe variations of operating point and faults in various locations.

Future power systems in terms of Cyber - Physical Energy Systems (CPES) apply the integration of IT and physical processes using local and wide area communication networks. The smart grid is a typical example of the application of CPES and poses additional challenges to the engineering as these networks consist of two components: the power system itself and an underlying communication network applied for transmitting monitoring and control information. Therefore, performance evaluations of CPES need to take into account both networks in detail in order to provide meaningful results. In this paper, we introduce our simulation environment INtegrated co-Simulation of Power and ICT systems for Real-time Evaluation (INSPIRE), which is based on the Hybrid Simulator Architecture [1] and capable of evaluating both power system and communication network within a co-simulation framework. Besides the simulator architecture, we detail our time synchronization approach, which is applied for interconnecting communication and power system simulation. Secondly, we present reference scenarios and configuration settings for the combined simulation system. Finally, we introduce the first performance evaluation carried out using INSPIRE, covering characteristics of the communication network and highlighting the retroactive effects on the power system using an exemplary control algorithm.

WAMS (Wide Area Measurement System) is a newly developed technology, which can provide a reliable guarantee to the simultaneous measurement of power system. But PMU (Phasor Measurement Unit) can not be widely used in power grid now, so it is impossible that WAMS can replace SCADA (Supervisory Control And Data Acquisition) system presently. Hence, how to incorporate WAMS measuring data into other systems including SCADA has become a hot research topic currently especially the data exchange between them. In order to solve this problem, a CIM (Common Information Model)-based information model for WAMS is proposed in this paper according both to IEC 61970: EMS-API (Energy Management System Application Program Interface) part 303 and the principle and the application of WAMS. This paper establishes a new package named WAMS in the framework of CIM and then some extended classes and their attributes along with their relationships are also defined. Finally this paper briefly describes the application of this CIM-based information model by presenting a flow diagram about information integration and utilization between WAMS and SCADA.