ADAPTIVE NEURO-FUZZY INTERFACE SYSTEM.doc (Size: 244 KB / Downloads: 120)
NEW TRENDS IN HVDC TRANSMISSION BASED ON ADAPTIVE NEURO-FUZZY INTERFACE SYSTEM (ANFIS)
The industrial growth of a world requires increased consumption of energy, particularly electrical energy. This has led to increase in the generation and transmission facilities to meet the increasing demand. In developing countries like India, the demand doubles every seven years which requires considerable investment in electric power sector. The problems of AC transmission particularly in long distance transmission, has led to the development of DC transmission. Modern HVDC systems combine the good experience of the old installations with recently developed technologies and materials. The result is a very competitive, flexible and efficient way of transmitting electrical energy with a very low environmental impact.
This paper presents an overview of new trends in HVDC transmission. It mainly focuses on Adaptive Neuro-Fuzzy Interface System (ANFIS).It is computationally simple and accurate expert system for fault identification of HVDC Converter and Control of HVDC system. Adaptive Neuro-Fuzzy Inference System (ANFIS) is applied and discussed in detail. Instead of using separate fault identifier for each valve, an integrated fault identifier is developed which is effective for complete bridge Converter. Fault identifiers are tested for HVDC with strong and weak ac sides. Fault identification methods are applicable in both inversion and rectification mode. ANFIS based current control is also developed for a HVDC system. Several digital simulation results are presented to validate the procedure outlined in the paper. ANFIS based control can be easily combined with the fault identifier to form integrated system, which can improve dynamic response of HVDC systems.
In recent years artificial intelligence based on Neural network, Fuzzy system, Adaptive Neuro-Fuzzy inference System (ANFIS), genetic algorithm, etc. have met growing interest in many industrial applications. Fault diagnosis of systems is a major subject of expert systems applications. The past two decades have revealed great advances in the application of artificial intelligence to power systems. A trend that is growing in visibility relates to the use of fuzzy logic in combination with neuro-computing and genetic algorithms. More generally, fuzzy logic, neuro-computing, and genetic algorithms may be viewed as the principal constituents of what might be called soft computing. Unlike the traditional hard computing, soft computing is aimed at an accommodation with the pervasive imprecision of the real world. Number of papers are available that deal with the application of artificial intelligence in the area of power systems. A hybrid scheme using Fourier linear combiner and fuzzy expert system for the classification of transient disturbance waveform in power system is presented. Different methods based on Artificial Neural Network (ANN) to identify various faults that may occur in HVDC converter. Modern controls based on Artificial Neural Network, Fuzzy system and
Genetic algorithm are found fast, reliable, can be used for protection against the line and converter faults and are gaining more interest in the field of HVDC transmission. HVDC systems traditionally use PI controllers with fixed gains. Although such controllers have certain disadvantages, they are rugged and operate satisfactorily for perturbations within a small operating range. On the other hand, ANN controllers have some specific advantages where by the use of ANN controller has been shown to introduce flexibility and fault tolerance into the performance of the controllers. ANN has attracted a great deal of attention because of their pattern recognition capabilities and their ability to handle noisy data. However, its ability to perform well is greatly influenced by the weight adaptation algorithm and the amount of noise in the data. The neural network architecture suffers from a large number of training cycles and computational burden.
Neural network has the shortcoming of implicit knowledge representation, whereas fuzzy logic systems are subjective and heuristic. A Neuro-fuzzy system is simply a fuzzy inference system trained by a neural network- learning algorithm. The learning mechanism fine-tunes the underlying fuzzy inference system. Fuzzy system faces difficulties like a lack of completeness of the rule base and a lack of definite criteria for selection of the shape of membership functions, their degree of overlapping, and the levels of data quantization. Some of these problems can be solved if the neural technique is used for fuzzy reasoning. The HVDC system traditionally uses PI controllers to control the DC current thereby keeping the power (current) order at the required level. Although these controllers undoubtedly are robust and are operating satisfactorily for many years, they are prone to changes in system parameters, delays or other non-linearties in the system and suffer from some limitations. This paper describes fault identification and protection of a HVDC converter using ANFIS based fault identifier (ANFLBI). A fuzzy logic based current controller (ANFLBC) for the fast and flexible control of an HVDC transmission link is also designed. Unlike other controllers, ANFIS controller does not require a mathematical model of the system to estimate control input under disturbance conditions. ANFLBC can be easily combined with ANFLBI to form integrated system. Power system reliability improves when HVDC converter faults are detected and eliminated before they deteriorate to a severe state.