Reliability analysis and economic equipment replacement appraisal for substation and sub-transmission systems with explicit inclusion of non-repairable failures

The modern society has come to expect the electrical energy on demand, while many of the facilities in power systems are aging beyond repair and maintenance. The risk of failure is increasing with the aging equipments and can pose serious consequences for continuity of electricity supply. As the equipments used in high voltage power networks are very expensive, economically it may not be feasible to purchase and store spares in a warehouse for extended periods of time. On the other hand, there is normally a significant time before receiving equipment once it is ordered. This situation has created a considerable interest in the evaluation and application of probability methods for aging plant and provisions of spares in bulk supply networks, and can be of particular importance for substations. Quantitative adequacy assessment of substation and sub-transmission power systems is generally done using a contingency enumeration approach which includes the evaluation of contingencies, classification of the contingencies based on selected failure criteria. The problem is very complex because of the need to include detailed modelling and operation of substation and sub-transmission equipment using network flow evaluation and to consider multiple levels of component failures. In this thesis a new model associated with aging equipment is developed to combine the standard tools of random failures, as well as specific model for aging failures. This technique is applied in this thesis to include and examine the impact of aging equipments on system reliability of bulk supply loads and consumers in distribution network for defined range of planning years. The power system risk indices depend on many factors such as the actual physical network configuration and operation, aging conditions of the equipment, and the relevant constraints. The impact and importance of equipment reliability on power system risk indices in a network with aging facilities contains valuable information for utilities to better understand network performance and the weak links in the system. In this thesis, algorithms are developed to measure the contribution of individual equipment to the power system risk indices, as part of the novel risk analysis tool. A new cost worth approach was developed in this thesis that can make an early decision in planning for replacement activities concerning non-repairable aging components, in order to maintain a system reliability performance which economically is acceptable. The concepts, techniques and procedures developed in this thesis are illustrated numerically using published test systems. It is believed that the methods and approaches presented, substantially improve the accuracy of risk predictions by explicit consideration of the effect of equipment entering a period of increased risk of a non-repairable failure.

Optimal HMM filtering and decision feedback equalisation for differential encoded transmission systems

In this paper conditional hidden Markov model (HMM) filters and conditional Kalman filters (KF) are coupled together to improve demodulation of differential encoded signals in noisy fading channels. We present an indicator matrix representation for differential encoded signals and the optimal HMM filter for demodulation. The filter requires O(N3) calculations per time iteration, where N is the number of message symbols. Decision feedback equalisation is investigated via coupling the optimal HMM filter for estimating the message, conditioned on estimates of the channel parameters, and a KF for estimating the channel states, conditioned on soft information message estimates. The particular differential encoding scheme examined in this paper is differential phase shift keying. However, the techniques developed can be extended to other forms of differential modulation. The channel model we use allows for multiplicative channel distortions and additive white Gaussian noise. Simulation studies are also presented.

An intelligent approach using support vector machines for monitoring and identification of faults on transmission systems

Power system disturbances are often caused by faults on transmission lines. When faults occur in a power system, the protective relays detect the fault and initiate tripping of appropriate circuit breakers, which isolate the affected part from the rest of the power system. Generally Extra High Voltage (EHV) transmission substations in power systems are connected with multiple transmission lines to neighboring substations. In some cases mal-operation of relays can happen under varying operating conditions, because of inappropriate coordination of relay settings. Due to these actions the power system margins for contingencies are decreasing. Hence, power system protective relaying reliability becomes increasingly important. In this paper an approach is presented using Support Vector Machine (SVM) as an intelligent tool for identifying the faulted line that is emanating from a substation and finding the distance from the substation. Results on 24-bus equivalent EHV system, part of Indian southern grid, are presented for illustration purpose. This approach is particularly important to avoid mal-operation of relays following a disturbance in the neighboring line connected to the same substation and assuring secure operation of the power systems.

Fault-Diagnostic System using Support Vector Machines for Power Transmission Systems

This paper presents an approach for identifying the faulted line section and fault location on transmission systems using support vector machines (SVMs) for diagnosis/post-fault analysis purpose. Power system disturbances are often caused by faults on transmission lines. When fault occurs on a transmission system, the protective relay detects the fault and initiates the tripping operation, which isolates the affected part from the rest of the power system. Based on the fault section identified, rapid and corrective restoration procedures can thus be taken to minimize the power interruption and limit the impact of outage on the system. The approach is particularly important for post-fault diagnosis of any mal-operation of relays following a disturbance in the neighboring line connected to the same substation. This may help in improving the fault monitoring/diagnosis process, thus assuring secure operation of the power systems. In this paper we compare SVMs with radial basis function neural networks (RBFNN) in data sets corresponding to different faults on a transmission system. Classification and regression accuracy is reported for both strategies. Studies on a practical 24-Bus equivalent EHV transmission system of the Indian Southern region is presented for indicating the improved generalization with the large margin classifiers in enhancing the efficacy of the chosen model.

Intelligent Approach for Fault Diagnosis in Power Transmission Systems Using Support Vector Machines

This paper presents an approach for identifying the faulted line section and fault location on transmission systems using support vector machines (SVMs) for diagnosis/post-fault analysis purpose. Power system disturbances are often caused by faults on transmission lines. When fault occurs on a transmission system, the protective relay detects the fault and initiates the tripping operation, which isolates the affected part from the rest of the power system. Based on the fault section identified, rapid and corrective restoration procedures can thus be taken to minimize the power interruption and limit the impact of outage on the system. The approach is particularly important for post-fault diagnosis of any mal-operation of relays following a disturbance in the neighboring line connected to the same substation. This may help in improving the fault monitoring/diagnosis process, thus assuring secure operation of the power systems. In this paper we compare SVMs with radial basis function neural networks (RBFNN) in data sets corresponding to different faults on a transmission system. Classification and regression accuracy is reported for both strategies. Studies on a practical 24-Bus equivalent EHV transmission system of the Indian Southern region is presented for indicating the improved generalization with the large margin classifiers in enhancing the efficacy of the chosen model.

Switching and fault transient analysis of 765 kV transmission systems

The analysis of electromagnetic transients arising in EHV/UHV power networks gives necessary information about the possible stresses on the different network components, which will determine their proper design, limits of operation as well as their pertinent protection strategies. This paper describes the transient analysis of 765 kV EHV transmission system which is a typical expansion in Indian power grid system. Considering various conditions, switching transient and fault transient studies are carried out. A FORTRAN version of EMTP is developed, to study a practical example, then a comparison with the results available in the literature is made.

Adaptively modulated optical OFDM modems utilizing RSOAs as intensity modulators in IMDD SMF transmission systems.

Detailed investigations of the transmission performance of adaptively modulated optical orthogonal frequency division multiplexed (AMOOFDM) signals converted using reflective semiconductor optical amplifiers (RSOAs) are undertaken over intensity-modulation and direct-detection (IMDD) single-mode fiber (SMF) transmission systems for WDM-PONs. The theoretical RSOA model adopted for modulating the AMOOFDM signals is experimentally verified rigorously in the aforementioned transmission systems incorporating recently developed real-time end-to-end OOFDM transceivers. Extensive performance comparisons are also made between RSOA and SOA intensity modulators. Optimum RSOA operating conditions are identified, which are independent of RSOA rear-facet reflectivity and very similar to those corresponding to SOAs. Under the identified optimum operating conditions, the RSOA and SOA intensity modulators support the identical AMOOFDM transmission performance of 30Gb/s over 60km SMFs. Under low-cost optical component-enabled practical operating conditions, RSOA intensity modulators with rear-facet reflectivity values of >0.3 outperform considerably SOA intensity modulators in transmission performance, which decreases significantly with reducing RSOA rear-facet reflectivity and optical input power. In addition, results also show that use can be made of the RSOA/SOA intensity modulation-induced negative frequency chirp to improve the AMOOFDM transmission performance in IMDD SMF systems.

Research and design of high-speed advanced analogue front-ends for fibre-optic transmission systems

In the last decade, we have witnessed the emergence of large, warehouse-scale data centres which have enabled new internet-based software applications such as cloud computing, search engines, social media, e-government etc. Such data centres consist of large collections of servers interconnected using short-reach (reach up to a few hundred meters) optical interconnect. Today, transceivers for these applications achieve up to 100Gb/s by multiplexing 10x 10Gb/s or 4x 25Gb/s channels. In the near future however, data centre operators have expressed a need for optical links which can support 400Gb/s up to 1Tb/s. The crucial challenge is to achieve this in the same footprint (same transceiver module) and with similar power consumption as today’s technology. Straightforward scaling of the currently used space or wavelength division multiplexing may be difficult to achieve: indeed a 1Tb/s transceiver would require integration of 40 VCSELs (vertical cavity surface emitting laser diode, widely used for short‐reach optical interconnect), 40 photodiodes and the electronics operating at 25Gb/s in the same module as today’s 100Gb/s transceiver. Pushing the bit rate on such links beyond today’s commercially available 100Gb/s/fibre will require new generations of VCSELs and their driver and receiver electronics. This work looks into a number of state‐of-the-art technologies and investigates their performance restraints and recommends different set of designs, specifically targeting multilevel modulation formats. Several methods to extend the bandwidth using deep submicron (65nm and 28nm) CMOS technology are explored in this work, while also maintaining a focus upon reducing power consumption and chip area. The techniques used were pre-emphasis in rising and falling edges of the signal and bandwidth extensions by inductive peaking and different local feedback techniques. These techniques have been applied to a transmitter and receiver developed for advanced modulation formats such as PAM-4 (4 level pulse amplitude modulation). Such modulation format can increase the throughput per individual channel, which helps to overcome the challenges mentioned above to realize 400Gb/s to 1Tb/s transceivers.

Exploiting Human Resource Requirements to Infer Human Movement Patterns for Use in Modelling Disease Transmission Systems: An Example from Eastern Province, Zambia

In this research, an agent-based model (ABM) was developed to generate human movement routes between homes and water resources in a rural setting, given commonly available geospatial datasets on population distribution, land cover and landscape resources. ABMs are an object-oriented computational approach to modelling a system, focusing on the interactions of autonomous agents, and aiming to assess the impact of these agents and their interactions on the system as a whole. An A* pathfinding algorithm was implemented to produce walking routes, given data on the terrain in the area. A* is an extension of Dijkstra's algorithm with an enhanced time performance through the use of heuristics. In this example, it was possible to impute daily activity movement patterns to the water resource for all villages in a 75 km long study transect across the Luangwa Valley, Zambia, and the simulated human movements were statistically similar to empirical observations on travel times to the water resource (Chi-squared, 95% confidence interval). This indicates that it is possible to produce realistic data regarding human movements without costly measurement as is commonly achieved, for example, through GPS, or retrospective or real-time diaries. The approach is transferable between different geographical locations, and the product can be useful in providing an insight into human movement patterns, and therefore has use in many human exposure-related applications, specifically epidemiological research in rural areas, where spatial heterogeneity in the disease landscape, and space-time proximity of individuals, can play a crucial role in disease spread.

Multistage and coordinated planning of the expansion of transmission systems

In this paper, an efficient genetic algorithm (GA) is presented to solve the problem of multistage and coordinated transmission expansion planning. This is a mixed integer nonlinear programming problem, difficult for systems of medium and large size and high complexity. The GA presented has a set of specialized genetic operators and an efficient form of generation of the initial population that finds high quality suboptimal topologies for large size and high complexity systems. In these systems, multistage and coordinated planning present a lower investment than static planning. Tests results are shown in one medium complexity system and one large size high complexity system.