Innovation concept for measurement gross error detection and identification in power system state estimation

In this study, the innovation approach is used to estimate the measurement total error associated with power system state estimation. This is required because the power system equations are very much correlated with each other and as a consequence part of the measurements errors is masked. For that purpose an index, innovation index (II), which provides the quantity of new information a measurement contains is proposed. A critical measurement is the limit case of a measurement with low II, it has a zero II index and its error is totally masked. In other words, that measurement does not bring any innovation for the gross error test. Using the II of a measurement, the masked gross error by the state estimation is recovered; then the total gross error of that measurement is composed. Instead of the classical normalised measurement residual amplitude, the corresponding normalised composed measurement residual amplitude is used in the gross error detection and identification test, but with m degrees of freedom. The gross error processing turns out to be very simple to implement, requiring only few adaptations to the existing state estimation software. The IEEE-14 bus system is used to validate the proposed gross error detection and identification test.

Identifying multiple interacting bad data in power system state estimation

This paper presents an intelligent search strategy for the conforming bad data errors identification in the generalized power system state estimation, by using the tabu search meta heuristic. The main objective is to detect critical errors involving both analog and topology errors. These errors are represented by conforming errors, whose nature affects measurements that actually do not present bad data and also the conventional bad data identification strategies based on the normalized residual methods. ©2005 IEEE.

Convergence Property of the Measurement Gross Error Correction in Power System State Estimation, Using Geometrical Background

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Power system sensitivity analysis for probabilistic small signal stability assessment in a deregulated environment

Deregulations and market practices in power industry have brought great challenges to the system planning area. In particular, they introduce a variety of uncertainties to system planning. New techniques are required to cope with such uncertainties. As a promising approach, probabilistic methods are attracting more and more attentions by system planners. In small signal stability analysis, generation control parameters play an important role in determining the stability margin. The objective of this paper is to investigate power system state matrix sensitivity characteristics with respect to system parameter uncertainties with analytical and numerical approaches and to identify those parameters have great impact on system eigenvalues, therefore, the system stability properties. Those identified parameter variations need to be investigated with priority. The results can be used to help Regional Transmission Organizations (RTOs) and Independent System Operators (ISOs) perform planning studies under the open access environment.

Automatic meter reading – benefits and applications

In liberalized electricity markets, which have taken place in many countries over the world, the electricity distribution companies operate in the competitive conditions. Therefore, accurate information about the customers’ energy consumption plays an essential role for the budget keeping of the distribution company and for correct planning and operation of the distribution network. This master’s thesis is focused on the description of the possible benefits for the electric utilities and residential customers from the automatic meter reading system usage. Major benefits of the AMR, illustrated in the thesis, are distribution network management, power quality monitoring, load modelling, and detection of the illegal usage of the electricity. By the example of the power system state estimation, it was illustrated that even the partial installation of the AMR in the customer side leads to more accurate data about the voltage and power levels in the whole network. The thesis also contains the description of the present situation of the AMR integration in Russia.

Three-phase harmonic distortion state estimation algorithm based on evolutionary strategies

This paper presents a new methodology to estimate unbalanced harmonic distortions in a power system, based on measurements of a limited number of given sites. The algorithm utilizes evolutionary strategies (ES), a development branch of evolutionary algorithms. The problem solving algorithm herein proposed makes use of data from various power quality meters, which can either be synchronized by high technology GPS devices or by using information from a fundamental frequency load flow, what makes the overall power quality monitoring system much less costly. The ES based harmonic estimation model is applied to a 14 bus network to compare its performance to a conventional Monte Carlo approach. It is also applied to a 50 bus subtransmission network in order to compare the three-phase and single-phase approaches as well as the robustness of the proposed method. (C) 2010 Elsevier B.V. All rights reserved.

Harmonic Distortion State Estimation Using an Evolutionary Strategy

This paper presents a new methodology to estimate harmonic distortions in a power system, based on measurements of a limited number of given sites. The algorithm utilizes evolutionary strategies (ES), a development branch of evolutionary algorithms. The main advantage in using such a technique relies upon its modeling facilities as well as its potential to solve fairly complex problems. The problem-solving algorithm herein proposed makes use of data from various power-quality (PQ) meters, which can either be synchronized by high technology global positioning system devices or by using information from a fundamental frequency load flow. This second approach makes the overall PQ monitoring system much less costly. The algorithm is applied to an IEEE test network, for which sensitivity analysis is performed to determine how the parameters of the ES can be selected so that the algorithm performs in an effective way. Case studies show fairly promising results and the robustness of the proposed method.

SVD Applied to Voltage Sag State Estimation

The method presented in this paper addresses the problem of voltage sag state estimation (VSSE). The problem consists in estimating the voltage sags frequency at non-monitored buses from the number of sags measured at monitored sites. Usually, due to limitations on the number of available voltage sag monitors, this is an underdetermined problem. In this approach, the mathematical formulation presented is based on the fault positions concept and is solved by means of the Singular Value Decomposition (SVD) technique. The proposed estimation method has been validated by using the IEEE 118 test system and the results obtained have been very satisfactory.

DYNAMIC MODEL BASED STATE ESTIMATION IN A HEAVY DUTY DIESEL AFTERTREATMENT SYSTEM FOR ONBOARD DIAGNOSTICS AND CONTROLS

Estimating un-measurable states is an important component for onboard diagnostics (OBD) and control strategy development in diesel exhaust aftertreatment systems. This research focuses on the development of an Extended Kalman Filter (EKF) based state estimator for two of the main components in a diesel engine aftertreatment system: the Diesel Oxidation Catalyst (DOC) and the Selective Catalytic Reduction (SCR) catalyst. One of the key areas of interest is the performance of these estimators when the catalyzed particulate filter (CPF) is being actively regenerated. In this study, model reduction techniques were developed and used to develop reduced order models from the 1D models used to simulate the DOC and SCR. As a result of order reduction, the number of states in the estimator is reduced from 12 to 1 per element for the DOC and 12 to 2 per element for the SCR. The reduced order models were simulated on the experimental data and compared to the high fidelity model and the experimental data. The results show that the effect of eliminating the heat transfer and mass transfer coefficients are not significant on the performance of the reduced order models. This is shown by an insignificant change in the kinetic parameters between the reduced order and 1D model for simulating the experimental data. An EKF based estimator to estimate the internal states of the DOC and SCR was developed. The DOC and SCR estimators were simulated on the experimental data to show that the estimator provides improved estimation of states compared to a reduced order model. The results showed that using the temperature measurement at the DOC outlet improved the estimates of the CO , NO , NO2 and HC concentrations from the DOC. The SCR estimator was used to evaluate the effect of NH3 and NOX sensors on state estimation quality. Three sensor combinations of NOX sensor only, NH3 sensor only and both NOX and NH3 sensors were evaluated. The NOX only configuration had the worst performance, the NH3 sensor only configuration was in the middle and both the NOX and NH3 sensor combination provided the best performance.

Novel methods of state estimation and parameter identification for high-performance motorcycles

This research activity aims at providing a reliable estimation of particular state variables or parameters concerning the dynamics and performance optimization of a MotoGP-class motorcycle, integrating the classical model-based approach with new methodologies involving artificial intelligence. The first topic of the research focuses on the estimation of the thermal behavior of the MotoGP carbon braking system. Numerical tools are developed to assess the instantaneous surface temperature distribution in the motorcycle's front brake discs. Within this application other important brake parameters are identified using Kalman filters, such as the disc convection coefficient and the power distribution in the disc-pads contact region. Subsequently, a physical model of the brake is built to estimate the instantaneous braking torque. However, the results obtained with this approach are highly limited by the knowledge of the friction coefficient (μ) between the disc rotor and the pads. Since the value of μ is a highly nonlinear function of many variables (namely temperature, pressure and angular velocity of the disc), an analytical model for the friction coefficient estimation appears impractical to establish. To overcome this challenge, an innovative hybrid solution is implemented, combining the benefit of artificial intelligence (AI) with classical model-based approach. Indeed, the disc temperature estimated through the thermal model previously implemented is processed by a machine learning algorithm that outputs the actual value of the friction coefficient thus improving the braking torque computation performed by the physical model of the brake. Finally, the last topic of this research activity regards the development of an AI algorithm to estimate the current sideslip angle of the motorcycle's front tire. While a single-track motorcycle kinematic model and IMU accelerometer signals theoretically enable sideslip calculation, the presence of accelerometer noise leads to a significant drift over time. To address this issue, a long short-term memory (LSTM) network is implemented.

Power cycling lifetime estimation of IGBT power modules based on chip temperature modeling

In this doctoral thesis, methods to estimate the expected power cycling life of power semiconductor modules based on chip temperature modeling are developed. Frequency converters operate under dynamic loads in most electric drives. The varying loads cause thermal expansion and contraction, which stresses the internal boundaries between the material layers in the power module. Eventually, the stress wears out the semiconductor modules. The wear-out cannot be detected by traditional temperature or current measurements inside the frequency converter. Therefore, it is important to develop a method to predict the end of the converter lifetime. The thesis concentrates on power-cycling-related failures of insulated gate bipolar transistors. Two types of power modules are discussed: a direct bonded copper (DBC) sandwich structure with and without a baseplate. Most common failure mechanisms are reviewed, and methods to improve the power cycling lifetime of the power modules are presented. Power cycling curves are determined for a module with a lead-free solder by accelerated power cycling tests. A lifetime model is selected and the parameters are updated based on the power cycling test results. According to the measurements, the factor of improvement in the power cycling lifetime of modern IGBT power modules is greater than 10 during the last decade. Also, it is noticed that a 10 C increase in the chip temperature cycle amplitude decreases the lifetime by 40%. A thermal model for the chip temperature estimation is developed. The model is based on power loss estimation of the chip from the output current of the frequency converter. The model is verified with a purpose-built test equipment, which allows simultaneous measurement and simulation of the chip temperature with an arbitrary load waveform. The measurement system is shown to be convenient for studying the thermal behavior of the chip. It is found that the thermal model has a 5 C accuracy in the temperature estimation. The temperature cycles that the power semiconductor chip has experienced are counted by the rainflow algorithm. The counted cycles are compared with the experimentally verified power cycling curves to estimate the life consumption based on the mission profile of the drive. The methods are validated by the lifetime estimation of a power module in a direct-driven wind turbine. The estimated lifetime of the IGBT power module in a direct-driven wind turbine is 15 000 years, if the turbine is located in south-eastern Finland.

Reinforcement Learning Approaches to Power System Scheduling

One major component of power system operation is generation scheduling. The objective of the work is to develop efficient control strategies to the power scheduling problems through Reinforcement Learning approaches. The three important active power scheduling problems are Unit Commitment, Economic Dispatch and Automatic Generation Control. Numerical solution methods proposed for solution of power scheduling are insufficient in handling large and complex systems. Soft Computing methods like Simulated Annealing, Evolutionary Programming etc., are efficient in handling complex cost functions, but find limitation in handling stochastic data existing in a practical system. Also the learning steps are to be repeated for each load demand which increases the computation time.Reinforcement Learning (RL) is a method of learning through interactions with environment. The main advantage of this approach is it does not require a precise mathematical formulation. It can learn either by interacting with the environment or interacting with a simulation model. Several optimization and control problems have been solved through Reinforcement Learning approach. The application of Reinforcement Learning in the field of Power system has been a few. The objective is to introduce and extend Reinforcement Learning approaches for the active power scheduling problems in an implementable manner. The main objectives can be enumerated as:(i) Evolve Reinforcement Learning based solutions to the Unit Commitment Problem.(ii) Find suitable solution strategies through Reinforcement Learning approach for Economic Dispatch. (iii) Extend the Reinforcement Learning solution to Automatic Generation Control with a different perspective. (iv) Check the suitability of the scheduling solutions to one of the existing power systems.First part of the thesis is concerned with the Reinforcement Learning approach to Unit Commitment problem. Unit Commitment Problem is formulated as a multi stage decision process. Q learning solution is developed to obtain the optimwn commitment schedule. Method of state aggregation is used to formulate an efficient solution considering the minimwn up time I down time constraints. The performance of the algorithms are evaluated for different systems and compared with other stochastic methods like Genetic Algorithm.Second stage of the work is concerned with solving Economic Dispatch problem. A simple and straight forward decision making strategy is first proposed in the Learning Automata algorithm. Then to solve the scheduling task of systems with large number of generating units, the problem is formulated as a multi stage decision making task. The solution obtained is extended in order to incorporate the transmission losses in the system. To make the Reinforcement Learning solution more efficient and to handle continuous state space, a fimction approximation strategy is proposed. The performance of the developed algorithms are tested for several standard test cases. Proposed method is compared with other recent methods like Partition Approach Algorithm, Simulated Annealing etc.As the final step of implementing the active power control loops in power system, Automatic Generation Control is also taken into consideration.Reinforcement Learning has already been applied to solve Automatic Generation Control loop. The RL solution is extended to take up the approach of common frequency for all the interconnected areas, more similar to practical systems. Performance of the RL controller is also compared with that of the conventional integral controller.In order to prove the suitability of the proposed methods to practical systems, second plant ofNeyveli Thennal Power Station (NTPS IT) is taken for case study. The perfonnance of the Reinforcement Learning solution is found to be better than the other existing methods, which provide the promising step towards RL based control schemes for practical power industry.Reinforcement Learning is applied to solve the scheduling problems in the power industry and found to give satisfactory perfonnance. Proposed solution provides a scope for getting more profit as the economic schedule is obtained instantaneously. Since Reinforcement Learning method can take the stochastic cost data obtained time to time from a plant, it gives an implementable method. As a further step, with suitable methods to interface with on line data, economic scheduling can be achieved instantaneously in a generation control center. Also power scheduling of systems with different sources such as hydro, thermal etc. can be looked into and Reinforcement Learning solutions can be achieved.

State estimation using model order reduction for unstable systems

The problem of state estimation occurs in many applications of fluid flow. For example, to produce a reliable weather forecast it is essential to find the best possible estimate of the true state of the atmosphere. To find this best estimate a nonlinear least squares problem has to be solved subject to dynamical system constraints. Usually this is solved iteratively by an approximate Gauss–Newton method where the underlying discrete linear system is in general unstable. In this paper we propose a new method for deriving low order approximations to the problem based on a recently developed model reduction method for unstable systems. To illustrate the theoretical results, numerical experiments are performed using a two-dimensional Eady model – a simple model of baroclinic instability, which is the dominant mechanism for the growth of storms at mid-latitudes. It is a suitable test model to show the benefit that may be obtained by using model reduction techniques to approximate unstable systems within the state estimation problem.

The state estimation of flow demands in gas networks from sparse pressure telemetry

This paper presents novel observer-based techniques for the estimation of flow demands in gas networks, from sparse pressure telemetry. A completely observable model is explored, constructed by incorporating difference equations that assume the flow demands are steady. Since the flow demands usually vary slowly with time, this is a reasonable approximation. Two techniques for constructing robust observers are employed: robust eigenstructure assignment and singular value assignment. These techniques help to reduce the effects of the system approximation. Modelling error may be further reduced by making use of known profiles for the flow demands. The theory is extended to deal successfully with the problem of measurement bias. The pressure measurements available are subject to constant biases which degrade the flow demand estimates, and such biases need to be estimated. This is achieved by constructing a further model variation that incorporates the biases into an augmented state vector, but now includes information about the flow demand profiles in a new form.

Conditioning and preconditioning of the optimal state estimation problem

Optimal state estimation is a method that requires minimising a weighted, nonlinear, least-squares objective function in order to obtain the best estimate of the current state of a dynamical system. Often the minimisation is non-trivial due to the large scale of the problem, the relative sparsity of the observations and the nonlinearity of the objective function. To simplify the problem the solution is often found via a sequence of linearised objective functions. The condition number of the Hessian of the linearised problem is an important indicator of the convergence rate of the minimisation and the expected accuracy of the solution. In the standard formulation the convergence is slow, indicating an ill-conditioned objective function. A transformation to different variables is often used to ameliorate the conditioning of the Hessian by changing, or preconditioning, the Hessian. There is only sparse information in the literature for describing the causes of ill-conditioning of the optimal state estimation problem and explaining the effect of preconditioning on the condition number. This paper derives descriptive theoretical bounds on the condition number of both the unpreconditioned and preconditioned system in order to better understand the conditioning of the problem. We use these bounds to explain why the standard objective function is often ill-conditioned and why a standard preconditioning reduces the condition number. We also use the bounds on the preconditioned Hessian to understand the main factors that affect the conditioning of the system. We illustrate the results with simple numerical experiments.