Identification of Induction Motors with Smart Circuit Breakers

The problem of estimating the parameters of induction motor models is considered, using the data measured by a circuit breaker equipped with industrial sensors. The measured data pertain to direct-on-line motor startups, during which the breaker acquires three-phase stator voltage and current derivative. This setup is novel with respect to previous contributions in the literature, where voltage and current (and possibly also rotor speed) are considered. The collected data are used to formulate a parameter identification problem, where the cost function penalizes the discrepancy between simulated and measured derivatives of the stator currents. The resulting nonlinear program is solved via numerical optimization, and a number of algorithmic improvements with respect to the literature are proposed. In order to evaluate the goodness of the obtained results, an experimental rig has been built, where the motor's voltages and currents are simultaneously acquired also by accurate sensors, and the corresponding identification results are compared with those obtained with the circuit breaker. The presented experimental results indicate that the considered industrial circuit breaker is able to provide data with high-enough quality to carry out model-based nonlinear identification of induction machines. The identified models can then be used for several further applications within a smart grid scenario

Online circuit breaker monitoring system

Circuit breakers are used in a power system to break or make current flow through power system apparatus. Reliable operation of circuit breakers is very important to the well-being of the power system. Historically this is achieved by regular inspection and maintenance of the circuit breakers. An automated online circuit breaker monitoring system is proposed to monitor condition, operation and status of high and medium voltage circuit breakers. By tracking equipment condition, this system could be used to perform maintenance only when it is needed. This could decrease overall maintenance cost and increase equipment reliability. Using high accurate time synchronization, this system should enable development of system-wide applications that utilize the data recorded by the system. This makes possible tracking sequence of events and making conclusions about their effect on-line. This solution also enables reliable topology analysis, which can be used to improve power flow analysis, state estimation and alarm processing

Self-Adaptive Autoreclosing Scheme usingI Artificial Neural Network and Taguchi's Methodology in Extra High Voltage Transmission Systems

Conventional automatic reclosures blindly operate for permanent, semi-permanent or transient faults on an overhead line without any discrimination after allowing some estimated time delay. Reclosing onto a line with uncleared fault often results in, not only loss of stability and synchronism but also damage to system equipments, as a consequence. The thesis focuses on methods to discriminate a temporary fault from a permanent one, and accurately determine fault extinctiontime in an extra high voltage (EHV) transmission line in a bid to develop a self-adaptive automatic reclosing scheme. The fault identification prior to reclosing is based on optimized artificial neural network associated with three training algorithms, namely, Standard Error Back-Propagation, Levenberg Marquardt and Resilient Back-Propagation algorithms. In addition, Taguchi's methodology is employed in optimizing the parameters of each algorithm used for training, and in deciding the number of hidden neurons of the neural network. To get data for training the neural networks, a range of faults are simulated on two case studies -single machine -infinite bus model (connected via EHVtransmission line) and a benchmark IEEE 9-bus electric system. The spectra of the fault voltage data are analyzed using Fast Fourier Transform, and it has been found out that the DC, the fundamental and the first four harmonic components can sufficiently and uniquely represent the condition of each fault. In each case study, the neural network is fed with the normalized energies of the DC, the fundamental and the first four harmonics of the faulted voltages, effectively trained with a set of training data, and verified with a dedicated testing data obtained from fault voltage signals generated on IEEE 14-bus electric system model. The results show the efficacy of the developed adaptive automatic reclosing scheme. This effectively means it is possible to avoid reclosing before any fault on a transmission line (be it temporary or permanent) is totally cleared

Synchronous Generator Stability Characterization for Gas Power Plants Using Load Rejection Tests

For power grid operators, knowing the transient response of the synchronous generators (SGs) included in their grids is important in order to simulate and monitor faults and other contingencies. However, the time constant of the automatic voltage regulator (AVR) and speed governors of SGs are not fast enough to show their transient dynamics in the case of a fault in the grid. This paper presents a fieldwork carried out in more than 60 gas power plants, where the response of their controllers was studied. These power plants are running and supplying electricity to the Spanish grid. The study consists of recording some SG responses in different situations, varying the AVR or the speed governor setpoints while the generator is running at no-load conditions, and also performing load rejection tests, achieving a real fault emulation. Once all the data are gathered, a fitting of the SG parameters is performed by computer simulations using GENSAL, GAST and SEXS models replicating the performed field tests. This work allows us to build an accurate network model for the whole power system and check which plants are having trouble in the case of contingencies in the grid

Harmonic Estimation Of Distorted Power Signals Using PSO – Adaline

In recent times, power system harmonics has got a great deal of interest by many Power system Engineers. It is primarily due to the fact that non-linear loads comprise an increasing portion of the total load for a typical industrial plant. This increase in proportion of non-linear load and due to increased use of semi-conductor based power processors by utility companies has detoriated the Power Quality. Harmonics are a mathematical way of describing distortion in voltage or current waveform. The term harmonic refers to a component of a waveform occurs at an integer multiple of the fundamental frequency. Several methods had been proposed, such as discrete Fourier transforms, least square error technique, Kalman filtering, adaptive notch filters etc; Unlike above techniques, which treat harmonic estimation as completely non-linear problem there are some other hybrid techniques like Genetic Algorithm (GA), LS-Adaline, LS-PSOPC which decompose the problem of harmonic estimation into linear and non-linear problem. The results of LS-PSOPC and LS-Adaline has most attractive features of compactness and fastness. . Our new proposed technique tries to reduce the pitfalls in the LS-PSOPC, LS-Adaline techniques. With new technique we tried to estimate the Amplitudes by Least square estimator, frequency of the signal by PSOPC and phases of the harmonics by Adaline technique using MATLAB program. Harmonic signals were estimated by using LS-PSOPC, PSOPC-Adaline. Errors in estimating the signal by both the techniques are calculated and compared with each other

Protection and fault location schemes suited to large-scale multi-vendor high voltage direct current grids

Recent developments in voltage source converter (VSC) technology have led to an increased interest in high voltage direct current (HVDC) transmission to support the integration of massive amounts of renewable energy sources (RES) and especially, offshore wind energy. VSC-based HVDC grids are considered to be the natural evolution of existing point-to-point links and are expected to be one of the key enabling technologies towards expediting the integration and better utilisation of offshore energy, dealing with the variable nature of RES, and driving efficient energy balance over wide areas and across countries. Despite the technological advancements and the valuable knowledge gained from the operation of the already built multi-terminal systems, there are several outstanding issues that need to be resolved in order to facilitate the deployment of large-scale meshed HVDC grids. HVDC protection is of utmost importance to ensure the necessary reliability and security of HVDC grids, yet very challenging due to the fast nature of development of DC faults and the abrupt changes they cause in currents and voltages that may damage the system components. This situation is further exacerbated in highly meshed networks, where the effects of a DC fault on a single component (e.g. DC cable) can quickly propagate across the entire HVDC grid. To mitigate the effect of DC faults in large-scale meshed HVDC grids, fast and fully selective approaches using dedicated DC circuit breaker and protection relays are required. As the speed of DC fault isolation is one order of magnitude faster than typical AC protection (i.e. less than 10 ms), there is a need for the development of innovative approaches to system protection, including the design and implementation of more advanced protection algorithms. Moreover, in a multi-vendor environment (in which different or the same type of equipment is supplied by various manufacturers), the impact of the grid elements on the DC fault signature may differ considerably from case to case, thus increasing the complexity of designing reliable protection algorithms for HVDC grids. Consequently, there is a need for a more fundamental approach to the design and development of protection algorithms that will enable their general applicability. Furthermore, following successful fault clearance, the next step is to pinpoint promptly the exact location of the fault along the transmission medium in an effort to expedite inspection and repair time, reduce power outage time and elevate the total availability of the HVDC grid. Successful fault location becomes increasingly challenging in HVDC grids due to the short time windows between fault inception and fault clearance that limit the available fault data records that may be utilised for the execution of fault location methods. This thesis works towards the development of protection and fault location solutions, designed specifically for application in large-scale multi-vendor HVDC grids. First, a methodology is developed for the design of travelling wave based non-unit protection algorithms that can be easily configured for any grid topology and parameters. Second, using this methodology, a non-unit protection algorithm based on wavelet transform is developed that ensures fast, discriminative and enhanced protection performance. Besides offline simulations, the efficacy of the wavelet transform based algorithm is also demonstrated by means of real-time simulation, thereby removing key technical barriers that have impeded the use of wavelet transform in practical protection applications. Third, in an effort to reinforce the technical and economic feasibility of future HVDC grids, a thorough fault management strategy is presented for systems that employ efficient modular multilevel converters with partial fault tolerant capability. Finally, a fault location scheme is developed for accurately estimating the fault location in HVDC grids that are characterised by short post-fault data windows due to the utilisation of fast acting protection systems.Recent developments in voltage source converter (VSC) technology have led to an increased interest in high voltage direct current (HVDC) transmission to support the integration of massive amounts of renewable energy sources (RES) and especially, offshore wind energy. VSC-based HVDC grids are considered to be the natural evolution of existing point-to-point links and are expected to be one of the key enabling technologies towards expediting the integration and better utilisation of offshore energy, dealing with the variable nature of RES, and driving efficient energy balance over wide areas and across countries. Despite the technological advancements and the valuable knowledge gained from the operation of the already built multi-terminal systems, there are several outstanding issues that need to be resolved in order to facilitate the deployment of large-scale meshed HVDC grids. HVDC protection is of utmost importance to ensure the necessary reliability and security of HVDC grids, yet very challenging due to the fast nature of development of DC faults and the abrupt changes they cause in currents and voltages that may damage the system components. This situation is further exacerbated in highly meshed networks, where the effects of a DC fault on a single component (e.g. DC cable) can quickly propagate across the entire HVDC grid. To mitigate the effect of DC faults in large-scale meshed HVDC grids, fast and fully selective approaches using dedicated DC circuit breaker and protection relays are required. As the speed of DC fault isolation is one order of magnitude faster than typical AC protection (i.e. less than 10 ms), there is a need for the development of innovative approaches to system protection, including the design and implementation of more advanced protection algorithms. Moreover, in a multi-vendor environment (in which different or the same type of equipment is supplied by various manufacturers), the impact of the grid elements on the DC fault signature may differ considerably from case to case, thus increasing the complexity of designing reliable protection algorithms for HVDC grids. Consequently, there is a need for a more fundamental approach to the design and development of protection algorithms that will enable their general applicability. Furthermore, following successful fault clearance, the next step is to pinpoint promptly the exact location of the fault along the transmission medium in an effort to expedite inspection and repair time, reduce power outage time and elevate the total availability of the HVDC grid. Successful fault location becomes increasingly challenging in HVDC grids due to the short time windows between fault inception and fault clearance that limit the available fault data records that may be utilised for the execution of fault location methods. This thesis works towards the development of protection and fault location solutions, designed specifically for application in large-scale multi-vendor HVDC grids. First, a methodology is developed for the design of travelling wave based non-unit protection algorithms that can be easily configured for any grid topology and parameters. Second, using this methodology, a non-unit protection algorithm based on wavelet transform is developed that ensures fast, discriminative and enhanced protection performance. Besides offline simulations, the efficacy of the wavelet transform based algorithm is also demonstrated by means of real-time simulation, thereby removing key technical barriers that have impeded the use of wavelet transform in practical protection applications. Third, in an effort to reinforce the technical and economic feasibility of future HVDC grids, a thorough fault management strategy is presented for systems that employ efficient modular multilevel converters with partial fault tolerant capability. Finally, a fault location scheme is developed for accurately estimating the fault location in HVDC grids that are characterised by short post-fault data windows due to the utilisation of fast acting protection systems

A Review of Fault Diagnosing Methods in Power Transmission Systems

Transient stability is important in power systems. Disturbances like faults need to be segregated to restore transient stability. A comprehensive review of fault diagnosing methods in the power transmission system is presented in this paper. Typically, voltage and current samples are deployed for analysis. Three tasks/topics; fault detection, classification, and location are presented separately to convey a more logical and comprehensive understanding of the concepts. Feature extractions, transformations with dimensionality reduction methods are discussed. Fault classification and location techniques largely use artificial intelligence (AI) and signal processing methods. After the discussion of overall methods and concepts, advancements and future aspects are discussed. Generalized strengths and weaknesses of different AI and machine learning-based algorithms are assessed. A comparison of different fault detection, classification, and location methods is also presented considering features, inputs, complexity, system used and results. This paper may serve as a guideline for the researchers to understand different methods and techniques in this field

Optimal fault location

Basic goal of power system is to continuously provide electrical energy to the users. Like with any other system, failures in power system can occur. In those situations it is critical that correct remedial actions are applied as soon as possible after the accurate fault condition and location are detected. This thesis has been focusing on automated fault location procedure. Different fault location algorithms, classified according to the spatial placement of physical measurements on single ended, multiple ended and sparse system-wide, are investigated. As outcome of this review, methods are listed as function of different parameters that influence their accuracy. This comparison is than used for generating procedure for optimal fault location algorithm selection. According to available data, and position of the fault with respect to the data, proposed procedure decides between different algorithms and selects an optimal one. A new approach is developed by utilizing different data structures such as binary tree and serialization in order to efficiently implement algorithm decision engine. After accuracy of algorithms is strongly influenced by available input data, different data sources are recommended in proposed architecture such as the digital fault recorders, circuit breaker monitoring, SCADA, power system model and etc. Algorithm for determining faulted section is proposed based on the data from circuit breaker monitoring devices. This algorithm works in real time by recognizing to which sequence of events newly obtained recording belongs. Software prototype of the proposed automated fault location analysis is developed using Java programming language. Fault location analysis is automatically triggered by appearance of new event files in a specific folder. The tests were carried out using the real life transmission system as an example