Fuzzy Based Active Power Filter To Improve The Power Quality Of Power System

Fuzzy Based Active Filter For Power Quality Mitigation

OVERVIEW

Active filters are widely used in electrical distribution system for reactive power compensation and voltage / current harmonic elimination. In this ARTICLE, a fuzzy logic controlled, three-phase shunt active filter to improve power quality by compensating reactive power and current harmonics required by a nonlinear load is presented. PI regulator is replaced by fuzzy logic controller to improve the dynamic performance of shunt active filter under varying load conditions. The advantage of fuzzy control is that it is based on defined linguistic rules and does not require any mathematical model of the system unlike the other traditional controller. The compensation process is based on source current sensing only, an approach different from conventional methods. The performance of fuzzy logic controller is compared with PI controller under dynamic load conditions. Simulated studies show that fuzzy controller is found suitable for steady state and transient conditions of load.

Keywords—Fuzzy, Shunt Active Filter, Power Quality

INTRODUCTION OF ACTIVE POWER FILTER

Electrical energy is one of the main elements for the economical development of society. The just aspirations of modern societies to economical growth have forced us to secure more continual energy resources[1]. On the other hand, climate change and international treaties aiming to reduce greenhouse gas emissions have prompted all of us to be increasingly concerned about energy efficiency and conservation. With the rapid growth of the information-based economy, widespread expansion of electronic devices has become a prevalent phenomenon in both the public and private sectors. Their suitability to perform various functions such as storage, management, processing and exchange of digital data and information, are essential support for information and communication technology (ICT)[2],[3]. Although the consumption of energy may increase, influenced by the increase in the necessary communications infrastructure, the use of renewable energy may contribute to a more rational consumption of energy, reducing the impact on the environment. The current trend toward miniaturization in microelectronics, increased processing speed and greater functionality results in a particular sensitiveness to certain   kinds of electromagnetic perturbations[4],[5]. Thus, this situation is not only bringing about a greater demand for electricity, but in addition higher levels of power quality and reliability (PQR) needs, in quantities and time frames that have not been experienced before[6]. The reliability of the power supply delivered by utilities varies considerably and depends on a number of external factors. This infrastructure was designed primarily to serve “analog electrical devices”, which are generally tolerant to voltage fluctuations in the power supply. However, the present electric power grid is unable to consistently provide the PQR level required by the “digital devices” of our information based economy[7].

The conventional methods use PI controller to regulate the DC bus voltage of VSI. Nemours methods have been proposed to replace PI control scheme such as optimal regulator control [12], sliding mode control [13], and model reference adaptive control. However, the design of these mentioned controllers depends on derived accurate mathematical models which are difficult to obtain. Also, these models do not give satisfactory operation under varying load condition with increased nonlinearity [8], [14], [15]. Artificial intelligence is one of the key area to solve such system complexity and make control more robust for transient conditions. Neural network, fuzzy logic, expert system, various other optimization methods are used for the improvement of power quality [15]. Fuzzy logic control (FLC) is one of the significant tool in control design originated by Zadeh [16]. The advantages of FLC over conventional controllers are high robustness, insensitivity to parameters variations, handling of non-linearity and independent on mathematical models [8], [15], [17]-[19].

II. BASIC APF PRINCIPLE

An APF is an electronic converter that produces and injects into the system the necessary harmonic components to cancel the harmonics of load current. An APF can be installed in the point of common coupling (PCC) of an AC system to compensate one or several loads. Once installed, the current harmonic circulation to the system is limited. Nowadays, APF development allows its application to compensate the reactive power, the negative sequence currents, and the harmonic currents. So, the APFs are generically named active power line conditioners (APLC).

Fig.1 Basic Mitigation Scheme

Fig. 1 shows the basic APF compensation scheme including nonlinear loads with a three-phase supply system. A current controlled VSI converter is used as an APF. APF is controlled to supply / extract compensating current to / from the utility (PCC). SAPF overcome the drawbacks of passive filters by using the switching mode power converter to perform the harmonic current elimination. SAPF are developed to suppress the harmonic currents and compensate reactive power simultaneously. The shunt active power filters are operated as a current source parallel with the nonlinear load. The power converter of SAPF is controlled to generate a compensation current, which is equal but opposite the harmonic and reactive currents produced from the nonlinear load. In this situation, the supply current is sinusoidal and in phases with supply voltage A voltage source inverter having IGBT switches and an energy storage capacitor is implemented with SAPF. The main aim of the SAPF is to compensate harmonics, reactive power and to eliminate the unwanted effects of non ideal ac mains supplies only unity power factor sinusoidal currents.

III. FUZZY CONTROLLER DESIGN

Fuzzy logic controller (FLC) is suitable for systems that are structurally difficult to model due to naturally existing non linear ties and other model complexities. This is because, unlike a conventional controller such as PI controller, rigorous mathematical model is not required to design a good fuzzy controller. The database, consisting of membership functions. Basically membership value should lies between 1 & O . The operations performed are fuzzification, interference mechanism and defuzzification. The interference mechanism uses a collection of linguistic rules to convert the input conditions into a fuzzified output. Finally defuzzification is used to convert the fuzzy outputs into required crisp signals.

The real capacitor voltage is compared with a prescribe value. Fuzzy setsare selected based on the error in the dc link voltage. We have chosen 7 by 7 membership function. For the feasibility of the program are as below:

1.       ND: Negative Big

2.      NM: Negative Medium

3.      NS: Negative Small

4.      ZE: Zero  

P      PS: Positive Small 

Are used.

Input and Output membership functions are same. Fuzzy interference is done by using membership Function those are as follow

Fig.2 Membership function used for input error and delta error

Fig.3 Membership function for output

The rules were described as below:

• IF VDC is LN THEN power is PB.

• IF VDC is SN THEN power is PL.

• IF VDC is ZE THEN power is PM.

• IF VDC is SP THEN power is PS.

• IF VDC is BP THEN power is ZE0

The input and output variables are transferred into linguistic variables. Control rule table is given below.Fig.5 Show the MATLAB Model of Fuzzy controller used for the proposed project.

Fig.4 Fuzzy Controller

The input and output variables are transferred into linguistic variables. Control rule table is given below.

Table 1 Control Rule for the Fuzzy Controller

The above can be summarized as for implementing FLC:

(1) First, scaling factors consist of the normalization gain for input and de-normalization gain is selected properly.

(2) Rules decision based on accuracy and complexity.

(3) Fuzzification, implication using mamdani’s operator and finally defuzzification to get desired output.

IV. SIMULATION RESULTS OF FUZZY BASED ACTIVE POWER FILTER

To simulate the proposed FLC based control scheme with reduced sensors, a model in MATLAB\ SIMULINKTM and Sim Power System Block set is developed. The complete 3 phase active filter system is composed using a supply source, a voltage source inverter, coupling and smoothing inductors with highly non-linear characteristic based load. Various simulations are carried out to verify the performance of the active power filter using proposed FLC and conventional PI controller with during steady-state and transient conditions. The system parameters selected for simulation studies are given in Table 2.

Table 2. System Parameter

Fig.5 THD with PI Controller

Figure 5 shows the SIMULATION result with respect to PI controller. The THD value is 4.36%.

Fig.6 THD with Fuzzy Logic Controller

The instant the filter is switched on the source current becomes sinusoidal having THD as per specified by IEEE standard from the stepped wave shape. The THD in the load current is 43.92%. THD of source current is reduced from 43.92 % to 3.32 %, which proves the effectiveness of proposed FLC (Fig.6).

V. CONCLUSION

A simple fuzzy logic based three-phase shunt active power filter for current harmonic elimination and reactive power compensation is presented in this paper. The performance of fuzzy logic controlled shunt APF has been studied and compared with the conventional PI controller. The steady state performance is comparable to the PI controller whereas transient response is found better than the PI controller. The system has fast dynamic response for varying load condition and harmonic spectrum is found well below 5%, harmonic limit imposed by the IEEE-519 standard.