This paper presents a new technique to design a Unified Power Flow Controller (UPFC) for power flow control and DC voltage regulation of an electric power transmission system which is based on a hybrid technique which combines a Radial Basis Function (RBF) neural network (online training) with the sliding mode technique to take advantage of their common features. The proposed controller does not need the knowledge of the perturbation bounds nor the full state of the nonlinear system. Hence, it is robust and produces an optimal response in the presence of system parameter uncertainty and disturbances. The performance of the proposed controller is evaluated through numerical simulations on a Kundur power system and compared with a classical PI controller. Simulation results confirm the effectiveness, robustness, and superiority of the proposed controller.
Presently, it is well established in the scientific community that the UPFC has the ability to increase the power flow capacity and improve the stability of an electric power transmission system through the proper design of its controller [
Faced with these difficulties, intelligent controls such as fuzzy logic and artificial neural networks have emerged as better alternatives to the conventional linear and nonlinear control methods. However, the complexities associated with the adaption of membership functions and computation requirements for defuzzification have hindered the application of fuzzy logic [
Artificial neural networks have an inherent capability to learn and store information regarding the nonlinearities of the system and to provide this information whenever required. This renders the neural networks suitable for system identification and control applications [
In [
From the above drawbacks, in this paper, a new hybrid approach which combines RBF neural network with the sliding mode technique to design a UPFC controller for power flow control and DC voltage regulation of an electric power transmission system with unknown bounds of system uncertainty and disturbances is proposed. The advantages of this design philosophy are that the controller is suitable for practical implementation and it makes the design useful for the real world complex power system. The remaining sections of this paper are organized as follows. In Section
Figure
UPFC in power system. (a) Schematic diagram of the UPFC system. (b) Single-phase representation of the UPFC system.
Using Park’s transformation and assuming that the instantaneous power is kept invariant and the sending-end voltage vector
Since the series and shunt converters of the UPFC are coupled through a common DC-link, if the losses in the converters are neglected, then the dynamic of the DC-link voltage can be expressed as [
It is clear from (
In this section, the method proposed in [
Let us consider the SISO first-order nonlinear system in the following form:
Let the desired smooth signal
The above desired controller (
The control signal (
Note that the term
For this purpose, the following neural controller is proposed in order to approximate the control signal
Consider the systems described by (
In order to apply the neurosliding controller described above to power flow control, UPFC sending-end bus voltage control and DC-link voltage control, the dynamic equations of the UPFC completely described by (
The reference values of the state variables are obtained as
We can design the neurosliding controller
The performance of the proposed nonlinear controller is evaluated through digital simulations using MATLAB/SIMULINK software. The power system used is a Kundur two-area four-machine power system shown in Figure
Kundur power system test.
In this case study, the initial complex power flow
Control response to step changes in real and reactive power flow references in the transmission line. (a) (i) Active power at bus B3. (ii) Reactive power at bus B3. (iii) Voltage magnitude at bus B2. (iv) UPFC DC-link voltage. (b) (i)
Control response to step changes in real and reactive power flow references in the transmission line. (a) (i)
In practice, the references values of the control power system remain constant and the quantities being controlled vary under the effect of load variation, disturbance, and other perturbations. In this case study, the load increases by
Control response to load variation. (a) (i) Active power at bus B3. (ii) Reactive power at bus B3. (iii) Voltage magnitude at bus B2. (iv) UPFC DC-link voltage. (b) (i) Active power at bus B2. (ii) Active power at bus B5. (iii) Reactive power at bus B2. (iv) Reactive power at bus B5.
In practice, it is not possible to measure a signal accurately due to the presence of noise. For this reason, the third case study investigates the robustness of the proposed nonlinear controller with respect to measurement noise (uncertainties). In this case study, all simulations are conducted under noise conditions in the measured line currents with the magnitude of the noise reaching about
Control response to measurement noise. (a) (i) Active power at bus B3. (ii) Reactive power at bus B3. (iii) Voltage magnitude at bus B2. (iv) UPFC DC-link voltage. (a) (i)
In this case study, a three-phase-to-ground fault is applied on bus-5 and the fault is cleared after 100 ms. Simulation results for this case study are shown in Figure
Control response to three-phase fault. (a) All generator rotor angle in COI. (b) All terminal generator voltage.
In this paper, a new hybrid approach which combines Radial Basis Function (RBF) neural network with the sliding mode technique has been used to design a Unified Power Flow Controller (UPFC) for power flow control, UPFC sending-end voltage control, and DC voltage regulation of an electric power transmission system. The RBF neurosliding mode control technique uses online training to get its optimal parameter values. The proposed technique is robust and does not need the knowledge of the perturbation bounds nor the full state of the nonlinear system. The performance of the proposed controller has been evaluated through simulations on a Kundur power system and compared with a classical PI controller. Simulation results have shown the effectiveness and satisfactory performance of the proposed controller in dealing with the perturbations considered. Future works should be targeted towards the extension of the proposed hybrid approach to a wide area interconnected power system for power oscillation damping.
The parameters of the UPFC are shown in Table PI controllers parameters are as follows:
Series converter: Shunt converter: DC-link: RBF controller parameters are as follows: The values of
Shunt converter | Parameters |
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Values | 100 | 255 | 60 |
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0.22 | |
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Series converter | Parameters |
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Values | 100 |
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60 |
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0.16 | |
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DC-link | Parameters |
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— | — |
Values | 40 | 1.0 | 750 | — | — |
The authors declare that they have no conflicts of interest.