On enhancing power systems PMU based data analysis and condition monitoring

A wide-area measurement system (WAMS) consists of advanced measurementtechnology,thelatestcommunicationnetworkinfrastructure,andintegratedoperationalframework. The supervisory control and data acquisition (SCADA) infrastructure forenergy-management system (EMS) has been widely used in power systems for a longtime. Some of the functionalities of an EMS are system state monitoring, tie-line biascontrol,andeconomicdispatch[1].However,inrecentyears,variousdeficienciesoftheexisting SCADA-based EMS (such as quasi-steady-state calculation, non-synchronizeddataacquisition,andrelativelylowdatatransmissionrate)havebeenpointedout.Thesedefects make it impossible to sample the global state of a power system in real time.As more and more wide-area blackouts are reported, it is clear that acquiring real-timeor wide-area state information would be needed in the future. The state informationin terms of phasors of voltages and currents from a distributed wide area in real timeis therefore critical for avoiding large-area disturbances by effecting wide-area controlbased on wide-area measurements.The main enabler of WAMS is phasor measurement unit (PMU) technology. Withthe innovation of PMU, the problem of measuring the phasor quantities simultaneouslyfromawideareaofdistributedsubstations,alsocalled‘synchrophasor’,hasbeensolved.At present, the PMU technology is one of the essential enablers for WAMS. It utilizesthe availability of high-precision synchronized clock sources – extracted from globalpositioning system (GPS) receivers and samples the instantaneous analogue – quanti-ties of voltage and current magnitudes and phase angles. Many utilities are installingPMUs at very high voltage substations, but the trend is that eventually PMUs will beused to monitor all high-voltage transmission lines. There are debates as to whether thewide-area or long-distance communications that communicate phasor data, or controlaction messages, should only be done via proprietary networks, or the existing Inter-net infrastructure with quality-of-service (QoS) differentiated delivery could be used.These questions require further simulation studies as well as financial and policy deci-sions which are not considered in this chapter. We concentrate mostly on evaluatingthe network architecture, the specific network medium and technology, the appropriate

When the power oscillation occurs, its dynamic characteristics will bring an adverse influence of the discrete Fourier transform (DFT), which reduces the estimation accuracy of phasor measurement units (PMUs). Accordingly, based on the superior performance of the Kaiser window in terms of eliminating the spectrum leakage of DFT, a modified dynamic phasor estimation algorithm is proposed to improve the accuracy of phasor estimations under the power oscillation. On one hand, the Taylor series is employed to express the dynamic characteristic of power oscillation signals. On the other hand, the Least squares method is used to construct the equation to solve the Taylor derivatives in the model. Various simulation and RTDS generated test results show that the proposed algorithm holds a superior dynamic estimation accuracy, especially under power oscillation conditions.

Time synchronized phasors obtained using Phasor Measurement Units (PMU) spread across wide areas have revolutionized power system monitoring and control. These synchronized measurements must be accurate and fast in order to comply with the latest IEEE standards for synchrophasor measurements. The speed at which a PMU provides an output depends on the group delay associated with that PMU and the permissible group delay in-turn decides the utility of a PMU for either control or measurement application. Based on the group delay compensation techniques, in the literature, two individual types of PMUs, such as causal and non-causal PMUs have been introduced. This paper presents an approach where both causal and non-causal PMUs are combined in an integrated PMU architecture. This method not only illustrates the group delay performance of two PMUs in a single module, but also can be used for multiple functions. In this environment several PMU algorithms have been compared with respect to their group delays and their effect on the response time. Application of the integrated PMU architecture to a four-machine 10-bus power system has been demonstrated using a six-input PMU with three-phase voltage and current signals as inputs. Different causal compensation schemes are introduced due to the availability of voltage and current-based frequency and ROCOF signals. Impact of these compensation schemes on PMU accuracy is evaluated through the Total Vector Error (TVE) index. The influence of these compensation schemes on measurements like power and impedance is also investigated. Finally, outputs from the integrated PMU architecture are fed into a Power System Stabilizer (PSS) to control the small-signal stability performance of a power system during dynamic conditions.

The pursuit of achieving total power network observability in smart grids using Phasor Measurement Units (PMUs) carries a significant promise of real-time Wide-Area Monitoring, Protection, and Control (WAMPAC). PMU applications eliminate periodical measurements, thereby increasing accuracy through a high sampling rate of the measured power systems quantities. The high costs of installation of PMUs for total power system observability presents a challenge in the implementation of PMUs. This is due to the expensive costs of PMU devices. This has led to a prominent optimal PMU placement (OPP) problem that researchers tirelessly aim to solve by ensuring a complete power network observability while using the least installed PMU devices possible. In this paper, a novel Binary Firefly Algorithm (BFA) based on the node degree centrality scores of each bus is proposed to minimize PMU installations. The BFA solves the OPP problem in consideration of the effect of Zero Injection Buses (ZIBs) under normal operation and single PMU outage (SPO). The robustness and efficiency of the proposed algorithm is tested on IEEE-approved test systems and visualized with a force-directed technique on an undirected power network graph. The proposed BFA yields the same but better optimal PMU numbers, obtained by existing meta-heuristic optimization techniques found in the literature for each of the IEEE test cases, as well as highlighting the cost–benefit of having a robust system against single PMU loss while considering the ZIB effect for an improved system measurement availability.

Frequency is an important parameter that plays a crucial role in the monitoring, protection and control of power system. It provides an indication of the interconnection's generation/load balance. Normally nominal frequency is either 50 Hz or 60 Hz in most of the countries. Any deviation of frequency from nominal is an indication of abnormal condition or generation/load imbalance. Synchronized phasor measurement unit (PMUs) are providing a promising future for accurate phasor and frequency estimation. Initially Synchrophasor estimation was the main thrust and after amendments in IEEE standards researchers could able to provide solution for frequency and rate of change of frequency (ROCOF) estimation. The main concern now is to achieve accurate, fast, less computationally complex and economic frequency and ROCOF estimation algorithm. So far different algorithms have been proposed for commercial PMUs and are being claimed as in compliance with IEEE standards C37.118.2011. These standards are providing benchmark for justifying accuracy of commercial PMUs. For effective monitoring and control of these PMUs, their operation under transient and large dynamic oscillation demands quick and highly accurate estimation without any discrepancy in estimated phasor value, irrespective of different manufacturers. This paper presents a comparative analysis of fundamental phasor estimation algorithms namely Basic FFT, Recursive DFT and Non-Recursive DFT under normal operating condition and in presence of noise and harmonics.

A precise digital algorithms based on Discrete Fourier Transforms (DFT) to calculate the phasor and frequency in real-time are proposed. The given algorithms that we called the Smart Discrete Fourier Transforms (SDFT) family not only having all of the advantages of DFT but it also smartly take frequency deviation into consideration. These make the SDFT more accurate than the other methods. Besides, SDFT is recursive and very easy to implement, so it is very suitable for use in real-time. We feed the program results to compared with SDFT and conventional DFT method for measurement of phasor and frequency to consolidate the claim benefits of SDFT.

Synchrophasor technology is an emerging and developing technology for monitoring and control of wide area measurement systems (WAMS). In an elementary WAMS, two identical phasors measured at two different locations have difference in the phase angles measured since their reference waveforms are not synchronized with each other. Phasor measurement units (PMUs) measure input phasors with respect to a common reference wave based on the atomic clock pulses received from global positioning system (GPS) satellites, eliminating variation in the measured phase angles due to distant locations of the measurement nodes. This has found tremendous applications in quick fault detection, fault location analysis, accurate current, voltage, frequency and phase angle measurements in WAMS. Commercially available PMU models are often proven to be expensive for research and development as well as for grid integration projects. This research article proposes an economic PMU model optimized for accurate steady-state performance based on recursive discrete Fourier transform (DFT) and provides results and detailed analysis of the proposed PMU model as per the steady state compliance specifications of IEEE standard C37.118.1. Results accurate up to 13 digits after decimal point are obtained through the developed PMU model for both nominal and off-nominal frequency inputs in steady state.

Phasor measurement unit (PMU) performance depends on the performance of the PMU’s analog input section and the limitations of its DSP algorithms. For the different PMU performance requirements established in the standards, the relative importance of these two factors varies significantly. For steady-state frequency and rate-of-change-of-frequency (ROCOF) measurements, this paper shows that estimation error is primarily dependent on noise mechanisms in the input section and the effective bandwidth of the signal processing algorithms. Estimation error may be predicted based on understanding these two factors, and optimum performance may be realized by careful design choices. When the PMU function is implemented with separate data acquisition and computation functions, this interaction must be carefully considered, so that the PMU results are equivalent to those from an integrated, conformant PMU – as users expect.

Phasor Measurement Units (PMUs) are normally used in Wide Area Measurement System (WAMS) for vast applications, such as monitoring the transmission system status over bulky areas including the control and protection. Therefore, this paper reviews the power system protection in relation to the synchronized PMUs and the applications of phasor estimation techniques. Furthermore, the paper elaborates the research works on the applications of PMU for diverse features. The structure and types of PMUs are described utilizing the mathematical modeling of simple Discrete Fourier Transform (DFT) with Recursive and Non-Recursive estimation methods. The paper also presents the results of some published studies for two algorithms (Recursive and Non-Recursive). The review has revealed that the Non-Recursive algorithm is more accurate and gives robust estimation results.

Introduction of Synchronized phasor measurement unit (PMU) in power system is a solution for monitoring system wide disturbances. PMUs provide precise information about a system’s phasors such as magnitude and phase angle of sine waves of the system. PMU data is a valuable source when determining the post-mortem of a fault, or a system disturbance. It can be unveiled what time the system began to falter and exactly what instabilities the system experienced. Knowing the cause of such instabilities will allow enhancement of the current system protection system. This will help in minimizing risk, disruptions or total system collapse. PMUs are synchronized via global positioning system (GPS). For wide area monitoring (WAM), synchronization of the PMUs allows for interconnected systems to be monitored simultaneously, giving real time records. This paper explores the impact of PMU in modern power system, deployment strategies of PMU network around Texas Tech University, importance of PMU data collected from the network, correlation of events by data analysis and focus on how the time stamped information is valuable for grid stability. The ongoing smart architecture of grid will increase dependency and importance on PMU based network in future.

The concept of Wide-Area Measurement Systems (WAMS) envisages the transmission of current and voltage phasor samples from remote Phasor Measurement Units (PMUs) to control centres, relays or controllers. Compression of the high frequency samples of sinusoidal waveforms into phasors is usually done using Discrete-Fourier Transform(DFT)-like algorithms. The phasor data is normally useful only when the system is in sinusoidal steady state, or nearly so. It is worthwhile to explore means to get reasonably accurate transient information as well, even if it means that some additional compressed information needs to be transmitted from the PMUs. Transient reconstruction has practical utility in the analysis of disturbances (at a remote control centre) and for flagging the initiation and clearing of network disturbances, which may be required for some WAMS analytics. In this paper, it is shown that one-cycle-delayed reconstruction of power system transient waveforms can be done with samples of the dc, fundamental and harmonic phasors reported at a rate of one sample per cycle obtained from the DFT algorithm. For power system transients, a few harmonics are usually adequate to reconstruct the transient waveforms with reasonable accuracy. The paper explains the reconstruction method, presents illustrative examples and discusses possible applications and practical issues.

Recently, many aspects of power system dynamic analysis, including oscillation identification, load model identification and system state estimation, are conducted based on the measurement data from phasor measurement unit (PMU). However, the impact of PMU measurement error, especially in power system dynamic process, has an assignable impact on these analysis results and thus cannot be neglected. In this paper, the requirements from IEEE standards and the analyze method for PMU measurement error are proposed first. Then, the ambient signal based identification method and the impact of measurement error for dynamic load model parameters is introduced. Afterwards, the stochastic distribution and frequency spectrum of PMU measurement error are measured through an experiment carried in a power system hardware simulation platform. Finally, the impact of PMU measurement error on the identification results of load model is simulated in WSCC 9 bus system.

The discrete Fourier transform (DFT) is a powerful phasor estimation algorithm conventionally used in digital relaying applications. The DFT‐based phasor estimation is usually accompanied by oscillatory error due to the presence of the decaying DC (DDC) components in fault current signals. Accordingly, two modified DFT‐based phasor estimation algorithms are proposed in this paper. The proposed algorithms accurately estimate the exact fundamental phasor by extracting the DDC components using successive outputs of the conventional DFT algorithm. The performance of the proposed algorithms is validated by the mathematically generated and PSCAD‐simulated signals and is compared with previous algorithms. The results demonstrate that the proposed algorithms in this paper are more robust than other ones. Moreover, they are applicable in the DFT algorithm with different data windows.

Population of Phasor Measurement Units (PMUs) in power systems are increasing day by day as PMUs measure the electrical quantities more accurately with time-stamping. The measurements done by PMU can be used for monitoring, controlling and for state estimation of the power system. Since the installation of PMUs demand high capital cost, their number and location to be chosen optimally is by minimizing investment without losing observability of the system. In this paper Integer Programming techniques used to solve Optimal Placement of PMU (OPP) problem. The OPP problem is solved for normal power system as well as for a few contingency conditions like one PMU outage, considering zero injection bus, outage of single line on various standard IEEE Bus Systems. The work is also trying to place PMUs under planned islanding in certain standard networks.

The characteristics of oscillation modes, such as interarea, regional, and subsynchronous modes, can vary during a power system fault, which can cause switching and control actions in the power system. Transient data of the modal response due to such a fault can be acquired through phasor measurement units (PMUs). When the transient data have a long duration, it is desirable to perform modal identification separately on each segment of the transient data, so as to reflect the varying characteristics of oscillation modes. A conventional discrete Fourier transform (DFT)-based method for parametric modal identification cannot be efficiently applied to such a segment dataset. In this paper, a DFT-based method with an exponential window function is proposed to identify oscillation modes from each segment of transient data in PMUs. This window function allows the application of the least squares method (LSM) for modal identification in the same manner as the conventional method. The accuracy of identification of the proposed method is compared with those of the conventional method and a Prony method through synthetic data of transient responses. Its feasibility is also verified by identifying real-world oscillation modes from transient data in PMUs.