State Estimator (SE) Implementation On a Distributed Computer System

It is pointed out that the uncertainty in an analog measurement is a combination of systematic error and random error; The systematic error completely dominates the random error and varies in a recognizable pattern over the range of measurement. A procedure is presented for reducing the systematic errors and estimating the prevailing standard deviations. A range of analog measurements is obtained from a transmission station, spanning peak and light-load conditions. Then the redundancies in measurements are used to formulate several functions relating these measurements with their attending errors. Minimization of these functions yields the required correction coefficients which are used to minimize the systematic errors and to evaluate the actual random errors, both of which are required by the state estimation. The approach developed uses voltage, real, and reactive power measurements which generally are available and used by state estimators. Current measurement is limited to a critical line or transformer per substation. The effectiveness of the proposed approach is confirmed by an experiment involving a 500/230 kV transmission station.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Distributed estimation is attracting more and more attention due to its scalability, robustness, and low power-consumption. In most distributed estimation algorithms, the output of the system is assumed to be noisy, while the input data is assumed to be accurate. However, in real applications, both of the input and output data may be perturbed by noise. Thus, it is unrealistic to assume that all the entries in the input data are accurate and only those in the output data are corrupted. In the cases of noisy input and output data, the total least-squares (TLS) method has the ability of minimizing the perturbations in both input and output data, and thus provides a better performance than the least-squares (LS)-based method. Besides, many nature and manmade systems present high level of sparsity. In this paper, we consider the case in which both the input and output data are corrupted by noise, and the parameter of interest is sparse. We present several sparsity-aware distributed TLS algorithms for the in-network cooperative estimation problem, in which the l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> - or l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -norm penalty term is used to exploit the sparsity of the signal. We then present theoretical analysis on the mean and mean-square performance of the proposed algorithms. In addition, several numerical simulations are given to verify the effectiveness and advantages of these proposed algorithms.

In modern power system control centers, operation and control of power systems are carried out with the help of real-time computers. Live data is captured by remote terminal units (RTUs) located at various stations and transmitted over suitable communication media to the control center for display, monitoring and control. Telemetering equipment forms a sizable component of the project cost. A methodology is presented in which RTUs are located at different stations to meet certain criteria such as observability of the system and absence of critical measurements. Additional reliability constraint of loss of information from a single RTU for the above two constraints is also imposed.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Operation engineers at EMS control center are very interested in the potential use of State Estimation (SE) technologies for identifying parameter and model errors, especially when encountering the scenario of an invalid SE solution or an SE solution with non-trivial residuals. Some network parameter and model errors, like an incorrect transformer tap or an analog measurement with flipped sign, can be identified early using relatively simple pre-processing approaches. But other errors may be detectable only after running SE solutions in real-time operation. This paper will share our experience in working with operation engineers to deal with these SE issues.

The success of a mission, in general terms of functionality of a complex system, is based on the probability that failure events do not happen during the mission. Therefore, a preliminary reliability analysis and consequently the definition of the reliability target are required. The reliability target of the whole system can be an input or output data of the reliability study; consequently, the reliability values of components are respectively output or input data. If we start from the definition of the target of the system (input data), considering similar ones (if available, especially for innovative ones!), we can allocate the reliability of each component (output data). The choice of the most suitable allocation technique is not a simple task, advantages and disadvantages are discussed in literature. Instead, if we start from the characteristics of the units available (input data), studying the structure (links in series and parallel), it is possible to calculate the reliability target of the whole system. However, these values could be not completely available (innovative systems) or refer to units with not comparable levels in terms of technology, complexity, safety and so on. In the manuscript, a new method is proposed to define the reliability target: Integrated Factors Method 2.0 (IFM 2.0). The purpose will be to combine the advantages of both above approaches. IFM 2.0 is an upgrade of IFM that was developed in 2002 by Falcone et al. The new methodology combines the old IFM method with new maintenance factors. Thanks to IFM 2.0, it is possible to estimate the reliability target of a system in a more confident way: “IFM Target 2.0.” The proposed method has been validated and applied to two complex systems. The obtained results show the effectiveness of the new method and its capability to overcome the problems of other previous methods.

Traditional power grid is currently transforming to next generation via Smart Grid (SG). For Smart Grid operation it is necessary to provide two-way communication with field devices. Internet of Things (IoT) trend with rising communication requirements from the aspect of bandwidth influences Smart Grid. The amount of data that will be exchanged between the Control Center and field devices is going to grow significantly in the near future. This will put additional load on communication infrastructure. One of the challenges that Smart Grid communication infrastructure needs to address is providing adequate quality of service (QoS) while taking in consideration available bandwidth and requirements for Smart Grid Operational Technology (SG OT) applications. This paper presents the influence of communication infrastructure congestion on Smart Grid. Special focus was put on State Estimation as one of the most critical real-time Smart Grid applications. State Estimation communication traffic is emulated and observed when the network is under different levels of congestion. Obtained test results on analyzed network demonstrates congestion impact on State Estimation. Congestion can severely impact State Estimation performance by providing outdated field information. The results and possible solutions to this problem are discussed.

With the emergence of the knowledge based society, great emphasis is put on the development of qualitative and quantitative policy tools for analysing the science and innovation system. In this chapter an overview is given of the available R&D input data at (supra) national and regional level. With the Frascati Manual the OECD provides a methodological framework for setting up national surveys to collect these data. This methodology is used to produce standardised measurements of human and financial resources devoted to R&D by OECD member countries. EUROSTAT adapted and extended this methodology to produce for the EU countries R&D input data at regional level. To measure the performance of national and regional R&D systems, input and output data have to be combined. The methodologies for collecting input and output data have, however, been developed largely independently from each other. The resulting limitations on their use in performance indicators are discussed, and suggestions are formulated for a more integrated approach to construct input and output data.

Static state estimators have been in use in control centres for over four decades now. Recent availability of synchronized phasor measurements for bus voltages and line currents allows development of dynamic state estimators which can track the frequency and synchronous machine state variables and complement the information provided by static state estimators on the network states. This chapter provides a brief overview of the static state estimation problem followed by an introduction to dynamic state estimation. The problem of dynamic state estimation is formulated and a dynamic state estimator whose implementation lends itself to large size power grids is described. The chapter also contains a discussion of observability analysis for dynamic state estimators which presents additional challenges due to the time varying and nonlinear nature of the system and measurement equations.

This work presents a distributed method for control centers to monitor the operating condition of a power network. Specifically we consider (static) state estimation problems, in which the state vector consists of the voltage magnitude and angle at all network buses. We consider the state to be linearly related to network measurements, which include power flows, current injections, and voltages phasors at some buses. We admit the presence of several cooperating control centers, and we design two distributed methods for them to compute the minimum variance estimate of the state given the network measurements. The two distributed methods rely on different modes of cooperation among control centers: in the first method an incremental mode of cooperation is assumed, whereas, in the second method, a diffusive interaction is implemented. These estimation methods, which are proved to converge in finite time, are further exploited to develop a distributed algorithm to detect corrupted data among network measurements.

State estimation is a key element in today's power systems for reliable system operation and control. State estimation collects information from a large number of meter measurements and analyzes it in a centralized manner at the control center. Existing state estimation approaches were traditionally assumed to be able to tolerate and detect random bad measurements. They were, however, recently shown to be vulnerable to intentional false data injection attacks. This paper fully develops the concept of load redistribution (LR) attacks, a special type of false data injection attacks, and analyzes their damage to power system operation in different time steps with different attacking resource limitations. Based on damaging effect analysis, we differentiate two attacking goals from the adversary's perspective, i.e., immediate attacking goal and delayed attacking goal. For the immediate attacking goal, this paper identifies the most damaging LR attack through a max-min attacker-defender model. Then, the criterion of determining effective protection strategies is explained. The effectiveness of the proposed model is tested on a 14-bus system. To the author's best knowledge, this is the first work of its kind, which quantitatively analyzes the damage of the false data injection attacks to power system operation and security. Our analysis hence provides an in-depth insight on effective attack prevention with limited protection resource budget.

A novel ANN-based distribution network state estimator

System state monitoring is crucial in order to maintain the system security, reliability and quality of a power system. Considering the limitation of monitoring devices in distribution network, state estimation technique could be employed. State estimation is an action of appraising an unknown system variables based on limited number of real-time measurements. The main objective of this paper is to find the suitable state estimation that able to estimate the state of the islanded distribution network. In this paper, state estimation for a distribution network is developed and enhanced by incorporating composite load model. By incorporating this model, the calculated state variables of the network will be more accurate (to obtain the most practical value of the system state variables). Moreover, the proposed state estimation results are compared with backward-forward sweep load flow technique to justify the system state variables.

In a bioprocess, for example a fermentation process, many biological reactions are always working in intracellular space and the control of such a process is very complicated. Bioprocesses have therefore been controlled by the judgment of the experts who are the skilled operators and have much experience in the control of such processes. Such experience is normally described in terms of linguistic IF-THEN rules. Fuzzy inference is a powerful tool for incorporating linguistic rules into computer control of such processes. Fuzzy control is divided into two types--direct fuzzy control of process variables, for example sugar feed rate and fermentation temperature, and indirect control via phase recognition. In bioprocess control the experts decide the value of controllable process variables such as sugar feed rate or temperature as output data from several state variables as input data. Fuzzy control is regarded as a computational algorithm in which the causal relationship between input and output data are incorporated. In Japan fuzzy control has already been applied to practical industrial processes such as production of pravastatin precursor and vitamin B2 and to the Japanese sake mashing process; these examples are reviewed. In addition, an advanced control tool developed from a study on fuzzy control, fuzzy neural networks (FNN), are introduced. FNN can involve complicated causality between input and output data in a network model. FNN have been proven to be applicable to a research in biomedicine, for example modeling of the complicated causality between electroencephalogram or gene expression profiling data and prognostic prediction. Successful results on this research will be also explained.

Guest Editorial: Situational awareness of integrated energy systems

Maintaining reliability during power system operation relies heavily on the operator’s knowledge of the system and its current state. With the increasing complexity of power systems, full system monitoring is needed. Due to the costs to install and maintain measurement devices, a cost-effective optimal placement is normally employed, and as such, state estimation is used to complete the picture. However, in order to provide accurate state estimates in the current power system climate, the models must be fully expanded to include probabilistic uncertainties and non-linear assets. Recognizing its analogous relationship with state estimation, machine learning and its ability to summarily model unseen and complex relationships between input data is used. Thus, a power system state estimator was developed using modified long short-term (LSTM) neural networks to provide quicker and more accurate state estimates over the conventional weighted least squares-based state estimator (WLS-SE). The networks are then subject to standard polynomial scheduled weight pruning to further optimize the size and memory consumption of the neural networks. The state estimators were tested on a hybrid AC/DC distribution system composed of the IEEE 34-bus AC test system and a 9-bus DC microgrid. The conventional WLS-SE has achieved a root mean square error (RMSE) of 0.0151 p.u. for voltage magnitude estimates, while the LSTM’s were able to achieve RMSE’s between 0.0019 p.u. and 0.0087 p.u., with the latter having 75% weight sparsity, estimates about ten times faster, and half of its full memory requirement occupied.