Fuzzy Logic Control

For the efficient operation of micro-CHP systems in domestic environment, different

criteria have to be considered: the non-linear behaviour of micro-CHP, operation of micro-

CHP during randomly varying electrical and thermal demand throughout the seasons and

the impact that it could have on the grid. Due to these complexities inherent in the

operation of micro-CHP, an intelligent technique is required for efficient operation. The

fuzzy logic technique which aims to imitate the aspect of human cognition can deal with

such complexities.

Fuzzy systems are based on fuzzy set theory and the associated techniques is pioneered by

Lotfi Zadeh [118, 119]. Fuzzy logic control has gained considerable popularity in recent

years. Fuzzy logic control is a knowledge-based approach consisting of linguistic If-Then

rules that can be constructed using the knowledge and experience of experts in the relevant

field. It can also exploit universal approximators that can realise nonlinear mappings.

Unlike classical control strategies, which is point-to-point control, fuzzy logic control is a

range-to-point or range-to-range control [120]. It cannot assure the global optimal

performance of a system but it is capable of effectively providing a near optimal

performance. Unlike Boolean logic, which describes that a given input is either a member

of a given set (logic 1) or not (logic 0), fuzzy logic solves problems that change anywhere

in the range from 0 to 1 [121]. Therefore, instead of sharp switching it offers smooth

relocation of the output signal when one rule dominates the other. As a result, fuzzy logic is

132 mathematical model is difficult to obtain since the rules can be designed based on

heuristics, intuition and human expertise [122].

The implementation of fuzzy logic control involves the following steps: fuzzification; a

fuzzy inference process and defuzzification. Fuzzification converts data (crisp data) into

fuzzy data or membership functions and these membership functions are combined with

control rules to derive fuzzy output using a fuzzy inference process. During defuzzification,

different methods are used to calculate each associated output.

Fuzzy logic is easy to implement, robust, flexible to change (in terms of inputs, outputs and

rules) and it does not require any offline work. It is a transparent and qualitative technique,

thus giving a simplified explanation of an operating technique. Fuzzy logic makes the

application of a human language allowable for problem description and their solutions

[123], however it requires good quality experiential knowledge and data about the

controlled system’s operating characteristics. It can be applied in many applications, especially when the system’s model is unknown/uncertain or when the input parameters are

unstable and highly variable [123]. This feature suits the purpose here as domestic energy

demands have such characteristics. Changes in weather conditions or occupant behaviour

could change the thermal energy demand (as modelled in Chapter 4) thus requiring

immediate response from the micro-CHP. Fuzzy logic is applied in the modelling of vague

systems which are imprecise and complex.

In terms of previous work, fuzzy logic based control techniques have been widely

investigated for fuel cells as a source of energy in hybrid electric vehicles [124, 125] and as

133 micro-CHP. In [22] and [126] fuzzy logic controller has been used for controlling the

power conditioning unit (DC–DC converter and DC–AC inverter circuits) where as in [51] fuzzy logic controller is used with an aim to minimise the operating cost and CO2 emission

of micro-CHP system. Consequently, fuzzy logic control would be developed and

investigated for the Stirling engine based micro-CHP in this chapter.

The main idea about the fuzzy logic is to efficiently manage the energy generated by the

micro-CHP system to fulfil the energy demands and help the local grid to maintain its

stability. This principle can guarantee minimising the total amount of primary energy used

and carbon emission. Minimising the total amount of primary energy used will result in

minimisation of the total operating cost. The fuzzy logic controller is formulated in a

generic form to allow its use for any micro-CHP system and any thermal energy demand

patterns with the flexibility of giving the desired (or maximum) set temperature for the

controlled room and hot water.

A fuzzy logic controller is designed in this work as the decision maker for the switching of

the micro-CHP unit using three input variables: key (controlled space) room temperature,

hot water temperature (i.e. water temperature inside the thermal storage device tank) and

the voltage of the local LV network. Figure 7.1 shows a simple block diagram of the

proposed fuzzy controller for the micro-CHP. In some literature [51], electricity demand of

the house is also considered as one of the most influential input variables for the operation

of the micro-CHP unit. The micro-CHP considered in this research is a Stirling engine-

based micro-CHP which is heat-led so the electrical load is not considered as an input for

134 Control

Signal

Figure 7.1 Fuzzy controller for micro-CHP