Vulnerability control of large scale interconnected power system using neuro-fuzzy load shedding approach
Introduction
The changing character of power systems with its increasing dependence on the interconnected transmissions grid has introduced many challenges, of which the most pressing is the need for significant improvements in power system operational security and control. Security of electricity supply networks has always been a key issue in the development of the power industry. In recent years, the increasing development of supervisory control and data acquisition systems and energy management systems, the growing number of market participants and the development of more complex market schemes have been increasingly reliant on information technologies and sensing equipment. Power networks are critically dependent on information and sensing equipment for system reliability, operation, protection and maintenance. Therefore, in emergency and abnormal conditions, a power system operator has to deal with a large amount of data and apply the most appropriate remedial control actions (Franco et al., 2001). However, due to emotional and psychological stress, an operator may not be able to adequately respond to critical conditions and make correct decisions. Mistakes can damage very expensive power equipment or worse still lead to the major emergencies and catastrophic situations. Clearly, there is a strong need for automated corrective procedures that can assist operators in vulnerability control.
Several cascading failures and large area blackouts occurring in recent years highlighted the need for vulnerability assessment of power systems. Analysis of recent widespread outages demonstrates system vulnerabilities, such as blackout incidents that occurred in Tokyo in July 1987, Western Interconnection System in the United States in 1996, the Brazilian power system in March 1999, Northeastern United States and Canada in August 2003 and the latest blackout in Spain in July 2007. These vulnerabilities were seen when a sequential series of so-called normal events quickly diminished acceptable security limits and reliability margins intended to protect against multiple contingencies. Most of such incidents are believed to be related to lack of vulnerability information about heavily stressed systems where large amounts of real and reactive powers are transported over long transmission lines and appropriate power sources are not available to maintain the system.
Vulnerability control of power systems is very important so that corrective measures can be taken for preventing deterioration in service quality. Analysis of recent widespread outages demonstrates that blackouts rarely happened and are usually caused by a sequence of low-probability disturbance which is generally not expected by system operators. If fast control actions such as load shedding and generation rejection are not taken proactively, the system may cascade and separate into unplanned islands (Miroslav et al., 2007). For power systems which are operated closer to their stability limits, it is desirable to use load shedding when there is a lack of adequate spinning reserve margin and a shortage of tie line capacity. In the case of power deficit in a power system, load shedding schemes using relays are used to disconnect appropriate amount of load and maintain system stability.
Different techniques have been proposed to solve the load shedding problem. The under frequency load shedding (UFLS) scheme is a common practice used by utilities around the world for protection against generation and load mismatch. When the system frequency drops to some predefined threshold, the UFLS relays start to shed the load according to its pre-selected amount. UFLS relays setting is based on some fixed values obtained from off-line simulation results. Some utilities have implemented under voltage load shedding (UVLS) to protect against voltage instability. This scheme is generally implemented by using under voltage relays at specific locations to disconnect load within a specific time, when local voltage drops below a pre-determined threshold. The use of voltage relays to implement UVLS for fast voltage collapse is not reliable since the relays would need to operate within a few cycles to avoid uncontrolled loss of load and cascading outages (Jeff, Abraham, Richard, & Satish, 2004). In recent years, load shedding schemes have been repackaged using breaker interlocks schemes integrated with programmable logic controllers (PLC). In the PLC-based load shedding scheme, load shedding is initiated based on the system frequency deviations and/or other triggers. The circuit breaker tripping can be programmed based on the system loading, available generation and other specific logics. However, this scheme is not viable because it is limited to monitoring sections of a power system that are connected to the data acquisition system and also it incurs increased hardware cost (Farrokh et al., 2005). Another method used to solve the steady state load shedding scheme is by using genetic algorithm in which the scheme is treated as an optimization problem with the objective of minimizing the difference between the load and generation active and reactive powers (Sanaye & Davarpanah, 2005).
The conventional load shedding techniques may not work as desired in emergency conditions due to the complexity and size of modern power systems. Therefore, alternative methods are required for solving certain difficult power problems where the conventional techniques have not achieved the desired speed and accuracy. Such techniques are referred to as computational intelligent techniques using fuzzy logic, neural network or expert systems. Fuzzy logic has been applied for safety analysis of power protection and automation system action (Manana, Toader, & Anatoli, 2004). The fuzzy expert system was proposed for voltage instability control to calculate the optimum and minimum ratio of load shedding (Sallam & Khafaga, 2002). In addition, a fuzzy logic stabilizer has been developed for stability control of a 1 kVA laboratory scale model of power system (Saud, Adel, & Abdullaziz, 2005).
In this research work, an intelligent load shedding scheme is proposed using neuro-fuzzy controller as a means for vulnerability control of large scaled interconnected power systems. The neuro-fuzzy controller considers two inputs and one output in which the inputs are the vulnerability index using power system loss (PSL) and the bus voltage magnitudes whereas the output is the amount of load to be shed for each contingency case. The performance of the proposed neuro-fuzzy controller in load shedding is investigated by comparing it with the fuzzy logic controller.
Section snippets
Fuzzy logic and neuro-fuzzy
Fuzzy logic has emerged as a promising tool for solving complicated problems dealing with systems whose behavior is very complicated to model. The mathematical modeling of fuzzy concepts was first presented by Lotfi Zadeh in 1965 to describe mathematically, classes of objects that do not have precisely defined criteria of membership (Hung, Nadipuran, Carol, & Elbert, 2003). Fuzzy set theory provides an excellent means for representing uncertainty due to vagueness in the available data or
Load shedding concept
During steady state operation of a power system, the power balance is always maintained. However, such balance of power may be disturbed by sudden changes in load or loss of generation. If at any stage, it is found that the current operating state of a power system is insecure where some or all of the system constraints are violated, then fast corrective actions need to be taken so as to bring the system back to a secure operating state. Initially, generators are re-dispatched optimally in a
Load shedding using fuzzy logic and neuro-fuzzy controllers
Conventional controllers are derived from control theory techniques based on mathematical models of a process. These controllers are characterized with design procedures and usually have simple structures to yield satisfying results. However, in a number of cases, when parameter variations take place or when there is no simple mathematical model, fuzzy logic based control systems have shown superior performance to those obtained by conventional control algorithms. The proposed controller for
Simulation results and discussion
For load shedding estimation using NFLC, the ANFIS output is associated with the amount of load shed in MVA p.u. In the ANFIS implementation, 70% of the data set is used for training, 20% for checking and 10% for testing. Each ANFIS is trained for 150 iterations because accurate result can be achieved in 150 iterations. The ANFIS is generated using 15 Gaussian membership functions for each input. The number of inputs and the number of membership functions determine the number of fuzzy rules and
Conclusion
This paper presented the outcome of research from the application of neuro-fuzzy technique for vulnerability control of large scale interconnected power systems. To counteract the problem of operating and managing large scale interconnected power systems in vulnerable condition, a new load shedding scheme is developed by means of using neuro-fuzzy controller to determine the optimal amount of load to be shed so that a power system can remain in a secure condition. Existing techniques used for
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