Table 1 Some proposed solutions to overcome the problems of the DPC strategy of DFIG.
From: Power regulation of variable speed multi rotor wind systems using fuzzy cascaded control
References | Type of study | The type of controller used to improve DPC performance | Type of turbine | Cons | Positives |
|---|---|---|---|---|---|
Simulation | New look-up table | Traditional turbine | Use energy estimation, Susceptibility to changing system parameters, energy ripples, and high THD of current | Simplicity, ease of implementation, fast dynamic response, and few gains | |
Simulation | Synergetic sliding mode controller (SMC) | MRWT | Complexity, capacity estimation, cost, number of gains, difficulty of completion, and response time | Reducing power surges, improving current quality, increasing robustness, and outstanding performance | |
Simulation | Fuzzy SMC technique | Traditional turbine | The number of rules of fuzzy logic (FL), the use of a mathematical model (MM)of the system, the number of gains, complexity, estimation of capabilities, difficulty of completion, and response time | High robustness, overcome DPC strategy problems, lower THD value | |
Simulation | Fractional-order proportional integral super-twisting SMC technique | Traditional turbine | Complexity, cost, energy estimation, difficulty of completion, number of gains, and response time | Overcome DPC strategy problems, robustness, and improving current quality | |
Simulation | Backstepping control (BC) | Traditional turbine | Complexity, estimation of energies, relying on the MM of the system, being affected by changing system parameters, difficulty of completion, and response time | Robustness, reduce power ripples, minimize the THD value of current | |
Experimental | Artificial neural network (ANN) | Traditional turbine | The number of internal layers needed to obtain good results, power estimation, and number of cells in each layer | Ease of implementation, fast dynamic response, does not depend on the MM of the system, a small number of gains, is not affected by internal and external factors of the system, reduces power ripples, minimizes the THD value of current | |
Experimental | Super-twisting control (STC) | Traditional turbine | Number of gains, estimation of capabilities, and response time | Reduce power ripples, and minimize the THD value of current, simplicity, and robustness | |
Simulation | Proportional-integral (PI) controller based on genetic algorithm (GA) and terminal sliding surface technique | MRWT | Estimating capabilities, number of gains, expensive, difficult to achieve, number of gains, and response time | Overcome the problems of DPC strategy, greatly increasing robustness and performance | |
Simulation | FL technique | Traditional turbine | The number of rules of FL, the number of gains, estimation of energies, and response time | High robustness, overcome DPC strategy problems, lower THD value | |
Simulation | SMC technique | Traditional turbine | Reliance on the MM, the phenomenon of chattering, complexity, the difficulty of completion, and the use of energy estimation | Reducing ripples, increasing robustness, improving performance and efficiency, and improving current quality | |
Simulation | Modified SMC technique | MRWT | Power estimation | Simplicity, ease of realization, high robustness, outstanding performance, small number of gains, fast dynamic response, and reduced overshoot | |
Simulation | Simplified STC technique | MRWT | |||
Experimental | Feedback PI controller | MRWT | |||
Simulation | Dual STC technique | MRWT | Power estimation, complexity, expensive, difficult to achieve, and large number of gains | Improving the values of overshoot and steady-state error (SSE), increasing the quality of current and power, improving robustness and performance, reducing the value of THD of current, and overcoming the problems of the DPC strategy | |
Simulation | Neural STC technique | MRWT | Determining the number of internal layers needed, the number of neurons in each layer, estimating energies, affected by changing system parameters, and response time Time to set gain values | ||
Experimental | Intelligent STC technique | MRWT | |||
Simulation | GA-based STC technique | – | |||
Simulation | Integral BC technique | Traditional turbine | Complexity, number of gains, use of the MM of the DFIG, difficulty of completion, and estimation of energies | Reducing the value of THD of current, and overcoming the problems of the DPC strategy Improving the values of overshoot and SSE, increasing the energy/current quality, improving robustness and performance | |
Simulation | Synergetic controller | Traditional turbine | The presence of ripples at the energy and current levels, affected by changing the parameters, the THD value of current, and power estimation | ||
Simulation | Neural PI controller | Traditional turbine | Choosing the type of neural network and learning algorithm, dynamic response, affected by changing parameters, power estimation, THD value of current, and reduced robustness in case of changing parameters | ||
Simulation | Fractional-order PI controller | Traditional turbine | The number of gains is affected by changing system parameters, the presence of ripples at the level of both power and current, and the estimation of powers | ||
Simulation | BC technique with nonsingular terminal sliding mode surface technique | MRWT | Complexity, a large number of gains, response time, power estimation, affected by changing parameters, dependence on the MM of the DFIG, and power estimation | ||
Simulation | Fractional-order neural controller | MRWT | Choosing the type of ANN and learning algorithm, and power estimation | Improving performance and robustness, increasing power and current quality, and improving the values of both SSE and overshoot Overcoming DPC strategy problems | |
Simulation | GA-based type-I FL controller | Traditional turbine | The number of FL rules, the number of gains, and the time for calculating parametric values, and power estimation | ||
Simulation | Sliding-backstepping mode control | Traditional turbine | Complexity, its dependence on the MM of the DFIG, difficulty of application, the problem of chatter, low quality of current and power in the robustness test, capacity estimation, and response time | ||
Simulation | Intelligent modified SMC technique | MRWT | Low current quality in robustness test and power estimation | ||
Simulation | Synergetic-PI control based on GA technique | MRWT | Complexity, large number of gains, estimation of powers, response time, and difficulty of completion | ||
Simulation | Super-twisting fractional-order terminal SMC technique | Traditional turbine | |||
Simulation | Thirde-order SMC technique | MRWT |
